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Ant algorithm for discrete optimization
Abstract
This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the AGO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
... ACO algorithm was developed by Dorigo in 1999 which based on social behavior of ant [19][20][21]. When compared to FA which using attraction, ACO used similar techniques as ABC which uses the concentration of route (quality of solutions). ...
... This concentration shows the quality of the solution which is generated for discrete combinatorial problems. The vector can be represented as: (19) Based on (19), ACO did used evaporation over time features to avoid premature convergence. The main technique by ACO is the probability of choosing a route and the evaporation rate [22]. ...
... This concentration shows the quality of the solution which is generated for discrete combinatorial problems. The vector can be represented as: (19) Based on (19), ACO did used evaporation over time features to avoid premature convergence. The main technique by ACO is the probability of choosing a route and the evaporation rate [22]. ...
span>Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems.
The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power
system operation.</span
... The ant colony algorithm is a simulated evolutionary algorithm proposed by M. Dorigo et al [21]. It has been adopted to solve combinatorial optimization problems such as traveling salesman problems, job-shop scheduling, and quadratic programming problems, but it is apt to appear premature convergence or stagnation behavior, moreover, it costs longer time than some other algorithms. ...
... It is better than the existing process route of the enterprise, and the priority order of each optimized processing meta is as follows. 21 The process route of this scheme has included the main processes of that part, according to the specific processing conditions and technical requirements, some auxiliary processes (such as material preparation, blanking, scribing process in the previous stage, the intermediate heat treatment process, inspection and storage in the later stage) just need to add in the scheme so as to constitute an acceptable process route that meet the manufacturing cost and carbon emission demands. ...
To improve the dynamic adaptability and flexibility of process route during manufacturing, a dynamic optimization method of multi-process route based on improved ant colony algorithm driven by digital twin is proposed. Firstly, on the basis of part manufacturing features analysis, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example forverification analysis, which shows that this method can obtain a process route that gets closer to practical production.
... Ant-system is not interested in simulation of ant colonies, but in the use of artificial ant colonies as an optimization tool. Therefore the agents used in ant-system have some different feature than real ants and they are called as 'artificial ants' [6]. Real ants are almost blind animals and it is found that they achieve food searching facility as a colony by the pheromone trails. ...
... Real ants are almost blind animals and it is found that they achieve food searching facility as a colony by the pheromone trails. But, artificial ants are not completely blind and they have some memory [ 6] . ...
A parameter optimisation is made in order to improve the solution performance of ant-system, a heuristic algorithm which was developed by Marco Dorigo and colleques, on flow-shop scheduling problems. Five different flow-shop scheduling problems are tested by a software programmed with Visual Basic. As a result of these tests, suitable values for the performance parameters of the ant-system; trail persistence (ρ), the relative importance of trail (α), the relative importance of visibility (β), are tried to be found due to their performance.
... Scientific works [18,19] are also known, the authors of which adapt various characteristics for ant agents. This allows for solving a wide range of discrete optimization problems based on taking into account many components of the production conditions of cargo delivery. ...
... This allows for solving a wide range of discrete optimization problems based on taking into account many components of the production conditions of cargo delivery. Conducted studies by scientists using ACO algorithms testify to the expediency of its use for solving discrete optimization problems taking into account many components of production conditions and solving large-scale transport problems [18,19]. ...
The article concerns the improvement of the ACO (Ant Colony Optimization) ant colony optimization algorithm for the formation of routes of vehicles for the procurement of food raw materials on the territory of the community during emergencies. The purpose of the study is to improve the algorithm for the formation of routes of vehicles for the procurement of food raw materials on the territory of the community during emergencies. The proposed algorithm is based on the classical algorithm of ant colony optimization ACO and, unlike it, takes into account real production conditions during emergencies. The task of the research is to create an algorithm for the formation of effective routes of vehicles for the procurement of food raw materials in the territory of the community during emergencies, as well as its comparison with the classic ACO algorithm for solving various problems of route formation. It was established that the use of the classic algorithm for the optimization of ant colonies ACO, or its known modernizations, does not provide a high-quality solution to the problem of forming routes of vehicles for harvesting food raw materials on the territory of the community during emergencies. This is due to incomplete consideration of specific production conditions. The improved route formation algorithm involves 8 steps and is based on the classic ACO algorithm. In contrast to it, it takes into account real production conditions (damaged sections of the roadway, the presence of partial passage of vehicles, traffic jams caused by an emergency, etc.). The rule of the classic ACO algorithm regarding the selection of the next point in the route using the probabilistic-proportional transition of the k-th ant from the i-th to the j-th node (farm producing food raw materials) is proposed, replaced by one that takes into account the state of production conditions (road surface) between individual nodes. This ensures an increase in accuracy and a decrease in the duration of route formation, as well as an increase in the quality of making appropriate management decisions. The obtained results regarding the comparison of the use of algorithms when solving transport problems with a different number of vertices indicate that the proposed algorithm provides a deviation of the total path in the route, which does not exceed 1%. The proposed algorithm reduces the decision-making time by up to 6% in the presence of up to 50 units of vertices, and by 12...15% in the presence of vertices from 51 to 100 units. The improved vehicle routing algorithm can be used in decision-making support systems to plan the procurement of food raw materials on the territory of the community during emergencies, which will increase their efficiency
... Karıncaların bu davranışı göstermelerinin sebebi, yolda biriken ve karıncaların yol tercihlerine etki eden feromon isimli kimyasaldır [12]. Köprüdeki yolların farklı uzunlukta olduğu ve köprü sayılarının birden fazla olduğu durumda feromon salgılama davranışı sayesinde en kısa yolun seçildiği gözlemlenmiştir [13]. ...
... Bu sayede diğer karıncalar da kısa yolu tercih etmiş olurlar. Bunun sonucunda karıncaların ilk başta yaptıkları rastgele seçimlerin önemi azalır ve karıncaların feromon takip etme eğilimleri ana çalışma mekanizması haline gelir [12]. Gerçek karıncalardaki bu davranış biçimi, optimizasyon problemlerini çözmek için yapay karınca kolonilerine uyarlanmıştır [14]. ...
İnsansız hava aracı sistemleri, geniş uygulama alanları nedeniyle son yirmi yılda giderek daha önemli bir teknoloji haline gelmiştir. Bu sistemler, otonom sürü insansız hava aracı filoları da dahil olmak üzere birçok uygulama için çok rotorlu, sabit kanatlı gibi farklı biçimlerde kullanılmıştır. Bu yayın doğal afetlerde afetzedelere yardım paketi ulaştırmak için otonom insansız hava araçlarının hedef paylaşımına odaklanmaktadır. İlk olarak ağ merkezli sürü insansız hava aracı sistemlerinde kontrol stratejileri incelenmiştir. Optimizasyondaki genelleştirilmiş atama ve çok kaynaklı genelleştirilmiş atama problemleri tartışılmış ve bu problemlerin farkı verilmiştir. Daha sonra yardım paketleri dağıtım problemi, çok kaynaklı genelleştirilmiş atama problemi olarak tanımlanmıştır. Karınca Kolonisi Optimizasyon metodunun temelleri tanıtılmıştır ve bu optimizasyon metodu, doğal afetlerde yardım paketlerinin dağıtımı problemini çözmek için önerilmiştir. Ayrıca hedef paylaşımında hedef atama sırasının önemi örnek bir senaryoda gösterilmiştir.
... As a population-based meta-heuristic, ant colony optimization (ACO) is an effective optimization technique for solving complex combinatorial optimization problems [9][10][11][12]. Considering the characteristics of the vehicle and cargo matching problem, we capture the matching decisions as a shortest path problem and apply ACO to the problem for the first time. ...
... Constraints (8) ensure that the weight of each cargo recommended for each vehicle driver cannot exceed the transport capacity of the vehicle. Constraints (9) ensure that the cargo is to be delivered to its destination within the delivery lead time. Constraints (10) require that the total weight of all the cargos recommended for one vehicle driver should not exceed g times the transport capacity of one vehicle. ...
With the development of the sharing economy, a lot of vehicle-cargo matching service platforms have been built to reduce supply-request information asymmetry and enhance the use of idle vehicles. It is a critical but challenging task for a logistics service platform to create matches automatically between vehicles and cargos and achieve high vehicle utilization. Conventional vehicle-cargo matching such as one-to-one matching and one-to-many matching cannot recommend multiple vehicle and cargo sources for both drivers and shippers simultaneously. In this paper, we investigate an intelligent approach to generate many-to-many vehicle-cargo matches and recommend them to the corresponding cargo owners and vehicle drivers. To this end, we develop a mathematical model for maximizing the platform’s matching rate and matching profit and propose an innovative ant colony optimization based on parameter tuning to solve the matching problem. A clustering technology combining bacterial foraging chemotaxis with the k-means algorithm is used to judge the state of the ant colony, and the parameters are adjusted adaptively to make the algorithm converge rapidly to the neighborhood of the global optimal solution. We then apply the randomicity and ergodicity of chaos to adjust the parameters, aiming to jump out of local optimum. Numerical results show that the proposed algorithm can achieve recommendation results that are accurate and stable compared with those of some other search methods and can provide satisfactory solutions for vehicle drivers and cargo owners.
... In section (5) local search heuristic methods. Branch and bound algorithm is given in section (6). Computational experience is given in section (7). ...
... The important property of evaporation is that it prevents premature convergence to a suboptimal solution. In this manner the ACO has the capability of "forgetting" bad solution of the search space [6]. The Ant colony optimization (ACO) Ant colony optimization (ACO) is a meta-heuristic uses artificial ants to good solution to difficult combinatorial optimization problem. ...
This paper considers of scheduling () jobs on single machine to minimize the sum penalty number of late jobs with minimum sum of weighted of completion time. To solve this lower bound and some dominance rule are derived and it is incorporated in a branch and bound (B&B) algorithm. We propose heuristic method to find near optimal solution. We also report on computational experience with the branch and bound. Also we develop compare and test different local search method (Threshold accepted (TA), Tabu search (TS) and Ant colony optimization (ACO) algorithm) for the problem. Computational experience is found that these local search algorithms solve problem to 5000 jobs with reasonable time. Keyword:-Scheduling Single machine, Bicriteria, Lexicographical optimization, Branch and bound, Local search methods المستخلص:-) دوىلأ مسألل مبأنر ار ,الأز بأذ خ الكنة,أ دوىلأ مسألل ُرست د البحث هذا في (أند هأي الوأوي أوهد ىا منة,أ أا كأنع اع مأ مسلل لمصغيز كنع اع ك لم ا الكث وىل ال رزاقأ مأ ود ىىصف,ن نضي الز الصيغ ىقوم,ن د الكسلل لوذه الحل ىالمقيأو المفأزل قأ رز فأي رسأمدوام دنا قيو ,ن اقمز فقو الكسلل هذه لحل (B&B) أو قيا مأ أود للأا نإلضأنف أي الكح البحأث رزاقأ اسأمدومت كأنع اع مأ ةبيأز أم مات الكسألل لحأل ل أن د قأ اليز هأذه فأي نت المفز لمحذي زه,ت الويك, (TA, TS and ACO) ا أي الكح البحأث رزاقأ د للأا صأل الحأل أند إل الكسأمدوج الأشم لمصأغيز سأمدومت 0555 سمأ فأي كأل وهد اليا الكنة, دوىل لكسنقل مسنهك قوج هذا ,ن ك معقيعد
... In this study, an ant-quantity system proposed by Dorigo et al. (1999) was adopted to simulate ants' secretion behavior in which the amount of pheromone secreted by each ant is a constant [29]. The pheromone concentration on the edge ( , ) will change with the time as follows: ...
... In this study, an ant-quantity system proposed by Dorigo et al. (1999) was adopted to simulate ants' secretion behavior in which the amount of pheromone secreted by each ant is a constant [29]. The pheromone concentration on the edge ( , ) will change with the time as follows: ...
The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity). This paper proposes an improved ant colony algorithm (IMACA) based band selection algorithm (IMACA-BS), to overcome the two shortcomings of the standard ACA. For the former problem, a pre-filter is applied to improve the heuristic desirability of the ant colony system; the Pearson’s similarity measurement of the degree of redundancy among the selected bands is taken as one of the terms in the heuristic function, and this further accelerates the convergence of the IMACA-BS. For the latter problem, a pseudo-random rule and an adaptive information update strategy are, respectively, introduced to increase the population diversity of the ant colony system. The effectiveness of the proposed algorithm was evaluated on three public datasets (Indian Pines, Pavia University and Botswana datasets), and compared with a series of benchmarks. Experimental results demonstrated that the IMACA-BS consistently achieved the highest overall classification accuracies and significantly outperformed other benchmarks over all of the three experiments. The proposed IMACA-BS is, therefore, recommended as an effective alternative for band selection of hyperspectral imagery.
... Both real and artificial ants modify some aspects of their environment. As real ants deposit pheromone on the path they visit, artificial ants change some numeric information of the problem states (Dorigo et al, 1999). This information takes into account the ant's current performance and can be obtained by any ant accessing the state. ...
... Also, the last row is the number of iterations required by the existing Ant Colony System Algorithm (E) and the Improved Ant Colony System Algorithm (I) respectively to converge to the optimal solution. 1 -3 -6 -8 -10 15 5 1 -3 -6 -9 -10 13 6 1 -3 -5 -8 -10 11 7 1 -3 -5 -9 -10 15 8 1 -2 -6 -8 -10 13 9 1 -3 -7 -9 -10 19 10 1 -4 -6 -8 -10 17 11 1 -4 -7 -8 -10 14 12 1 -4 -5 -9 -10 15 13 1 -4 -5 -8 -10 11 14 1 -2 -6 -9 -10 11 15 1 -4 -6 -9 -10 15 16 1 -2 -7 -9 -10 21 17 1 -2 -7 -8 -10 18 The existing ACS takes 140 iterations to converge to optimal whilst the improved ACS takes 129 ...
Shortest Path Problems (SPP) are concerned with finding a path with minimum distance from one or more sources to one or more destinations through a network. With the increasing application of shortest path algorithms to network problems in real life, researchers and practitioners have begun to look outside the traditional algorithms, such as label setting and label correcting, which have some deficits compared with an algorithm such as Ant Colony. In this paper, an improved ant colony system meta-heuristic algorithm for solving SSP has been presented with modifications made in the following areas: the introduction of dynamic programming into the heuristic information, and the application of a ratio approach to the local pheromone update process. A hypothetical network problem of ten nodes with twenty edges was used as a test case. The results show that the improved ant colony algorithm outperforms the existing one in terms of the number of iterations required to converge to optimality.
... The ACO is an evolutionary stochastic computational discipline well adaptable for solving hard combinatorial optimization problems. Inspired from the natural behavior of ants in finding the shortest distance between their nests and food sources, it's based on indirect communication within a colony of simple agents, called (artificial) ants which exchange information about good routes through a chemical substance called pheromone that accumulates for short routes and evaporate for long routes [23,24]. ...
... For solving such problems, ants randomly select the vertex to be visited. When ant k is in vertex i, the probability of going to vertex j is given by expression (1) [23,24]. ...
In this paper, we present an optimization algorithm based on a BAcktracking Ant Colony Optimization (BA-ACO) technique for dealing with the active analog filter design. The BA-ACO algorithm is applied to the Low-Pass Butterworth filter design, realized with components (Resistors and capacitors) selected from different manufactured series, to satisfy the filter design criteria. PSPICE simulations are used to validate the obtained result/performances. A comparison with published works is highlighted
... In recent years, researchers have proposed many bio-inspired meta-heuristic consolidation algorithms, such as Ant Colony Optimization (ACO) algorithm, Genetic Algorithm, Artificial Bee Colony (ABC) algorithm, which are effective in solving large-scale problems and avoiding local optimal solutions. Ant Colony System (ACS) [6], [7], [8], a kind of ACO algorithm, finds the near-optimal solution in polynomial time complexity through probabilistic search in the solution space, which has attracted more and more attention for the excellent performance in solving NP-hard problems and combinatorial optimization problems. ...
... In each iteration, nA ants build new mapping relationships between VMs and hosts in parallel by sequentially performing VM consolidation for overloaded hosts and underloaded hosts (line 5-31). The ant first selects the VM in the overloaded host for redeployment (lines [5][6][7][8][9][10][11][12][13][14][15][16][17]. Under the premise of ensuring service performance, VMs in the underloaded hosts are redeployed to achieve energy saving (line [18][19][20][21][22][23][24][25][26][27][28][29][30]. ...
With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection polices according to the host load status. Extensive experiment is conducted to compare our proposed approach with the stateof-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates, achieving better comprehensive performance.
... The experiments performed use instances of the Traveling Salesman Problem (TSP). As posed in Dorigo, Caro, and Gambardella (1999) , Ant-Q is an algorithm that tries to merge Ant System (AS) algorithm and Q-learning properties. In its turn, the AS algorithm is the initial proposal of the Ant Colony Optimization metaheuristic ( Dorigo & Stützle, 2019 ). ...
... The objective is that the agents of the Ant-Q algorithms cooperate to learn AQ-values, thus seeking good solutions for the TSP. However, as indicated in Dorigo et al. (1999) and Dorigo and Stützle (2019) , besides having good performance, due to some aspects of Ant-Q, in particular the pheromone update rule, could be strongly simplified without affecting performance, Ant-Q was abandoned in favor of the simpler and equally good ACS (Ant Colony System), introduced in Dorigo and Gambardella (1997) . Meignan et al. (2008) propose the Agent Metaheuristic Framework (AMF). ...
This article presents a multi-agent framework for optimization using metaheuristics, called AMAM. In this proposal, each agent acts independently in the search space of a combinatorial optimization problem. Agents share information and collaborate with each other through the environment. The goal is to enable the agent to modify their actions based on experiences gained in interacting with the other agents and the environment using the concepts of Reinforcement Learning. For better introduction and validation of the AMAM framework, this article uses the instantiation of the Vehicle Routing Problem with Time Windows (VRPTW) and the Unrelated Parallel Machine Scheduling Problem with Sequence-Dependent Setup Times (UPMSP-ST), i.e., two classic combinatorial optimization problems. The main objective of the experiments is to evaluate the performance of the proposed adaptive agents. The experiments confirm that the ability to learn attributed to the agent directly influences the quality of solutions, both from the individual point of view and from the point of view of teamwork. In this way, the framework presented here is a step forward in relation to the other frameworks of the literature regarding to the adaptation to the particular aspects of the problems. Additionally, the cooperation between agents and their ability to influence the quality of the solutions of the agents involved in the search of the solution is confirmed. The results also strengthen the issue of the scalability of the framework, since, with the addition of new agents, there is an improvement of the solutions obtained.
... For solving such problems, ants randomly select the vertex to be visited. When ant k is in vertex i, the probability of going to vertex j is given by expression (1) [19,20]. ...
... where (19) (20) β and αi (i=1, 2… 5) are the coefficients depend on the inductor topology. The coefficients for square inductor are [24,25]: ...
... Ants left behind ongoing concentration traces linked to their journeys across time during the early expeditions. They can more easily calculate the shortest path to food as a result of the increase in track density on shorter pathways [23]. Figure 4 depicts the major workflow for the proposed ACOA algorithm. ...
It is possible to reduce distribution losses by strategically placing and sizing DG and BESS sources. Assuring low loss requires strategically placing the aforementioned devices; otherwise, the system may experience either under- or overvoltage. It is preferable to choose bus stations with less risk for loss. The proposed approach tries to pinpoint the optimal BESS size and placement to cut down on investment and operating expenses while still achieving the desired level of energy reduction. The development of optimisation algorithms for finding and scaling BESS units is the fundamental focus of this study. Two such strategies are being explored here: the Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACOA). The goal function, like the original issue, seeks to minimise system-wide power losses while adhering to specified levels of equality and inequality. This article explores the appropriate capacity and placement of the DGs in a 33-bus radial distribution grid to reduce power dissipations. Matlab code is used to perform a simulation, and the results are put to use gauging the method's sturdiness.
... In metaheuristics, evolutionary computing and genetic algorithms have been considered [14]. In evolutionary computing, various nature-inspired methods have been considered such as the particle swarm optimization (PSO) [8] algorithm and the ant colony optimization (ACO) [3] algorithm. However, both of these methods require many solution derivations, and when the time to calculate a solution each time is lengthy, computational time can become excessive. ...
We consider an air conditioning system design that minimizes the costs of electricity and equipment, while satisfying individual usage requirements. The problem of finding an optimal configuration can be formulated as a combinatorial optimization problem. When the cost of electricity is evaluated using simulation software, computational time is excessive. We therefore propose an efficient iterated local search method that uses sparse estimation and extreme statistics to reduce the computational time for evaluating the cost of electricity, and which makes use of the information obtained during a local search. In addition, Thompson's update probability is used for effective neighborhood selection.
... Traditional ACO is probability-based, which is used to find the optimal path of the graph [22,23]. When ants move from the initial point i to the end point j, the probability of ants choosing the path (i, j ) is determined by pheromone of the edge (i, j) and local heuristic information. ...
Electric power is widely used as the main energy source of ship integrated power system (SIPS), which contains power network and electric power network. SlPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization (MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.
... This evaporation process prevents the algorithm from premature convergence. ACO algorithm can discover good solutions very fast and it is robust in finding optimal solutions to discrete multi-objective optimization problems [68,164]. In the context of fog computing, some problems have been addressed using ACO-based algorithms. ...
... Ants choose the vertex to travel to by applying the state transition rule [19,20]. Ant k in node a will move to vertex b according to the probability offered by (1): while J k a corresponds to the list of possible moves, which are possible for ants starting form vertex a, ab τ represents the intensity of the trace associated to brink (a, b). ...
Ant Colony Optimization (ACO) algorithm, a well-known robust technique to solve easily both a simple and multiple objective optimization problems. This article presents an application of the ACO in order to achieve the optimal sizing of analog circuit. The proposed technique is employed to optimizing the sizing of a positive second-generation current conveyor (CCII+). Results show better objective functions than previously achieved by other optimization procedure. PSPICE simulations are used to confirm the validity of the reached optimal results and the accuracy of the proposed procedure.
... dedi" [18]. Temel ilkeleri ilk kez Marco Dorigo tarafından ortaya atılmış olan karınca kolonisi algoritmaları, karınca kolonilerinin feromon salgılayarak yiyecek kaynakları ile yuvaları arasındaki en kısa yolu bulma yöntemlerinden esinlenerek oluşturulmuş bir algoritmadır [19]. Karıncalar, yiyecek kaynaklarından yuvalarına en kısa yolu görme duyularını kullanmadan bulma yeteneğine sahiptirler. ...
Bu sohbetimizde Kur’ân-ı Kerim Işığında Algoritma ve Yapay Zeka Algoritmaların dan bahsedilmiştir.
... Ant colony optimization involves a colony of artificial ants which are abstractions from the behavioral traits of real ants cooperating in finding better solutions to different discrete optimization problems. The similarities between real ants and artificial ants which is exploited in ACO [16] include: ...
In this paper a Decision Support System named W8st colSoft is developed by employing dynamic programming and swarm intelligence model encoded in Visual Basic Studio 10.0. Solved solutions from literature were used to validate the developed decision support system. The results obtained from these validation presented an average error margin of 2.58% when compared with that from literature. Also, in order to present the scalability of the swarm intelligence model employed in the developed decision support system, it was used as a decision tool to analyze the collection of solid waste of the University of Port Harcourt three campuses as a whole, unlike recent publication where it
was analyzed Campus-wise. The resultant optimal path from the analysis presented a total distance of 15,682 m saving a total distance of 17.15 m when compared with other route options. Additionally, an Evolutionary Algorithm in Microsoft Excel 2013 was applied to the University of Port Harcourt four campus segments and the results were compared with those of the Proposed model. The percentage error margin between Evolutionary Algorithm and the Proposed model prediction ranges from -0.34 to 11.27. The Proposed model was able to achieve optimum value with minimum number of iterations in all cases and this is an advantage.
... The Dijkstra's algorithm and ant colony algorithm will be described in detail in 512 this section, the advantage of Dijkstra's algorithm is that it is easy to be used to solve 513 the shortest path problem in a network (Knuth et al., 1977), however, this algorithm 514 cannot solve the logistics path optimization problem with obstacles. Whereas the 515 advantage of the ant colony algorithm is that it enables to be used to solve the 516 logistics path optimization problem in discrete space (Dorigo et al., 1999). Therefore, 517 this paper makes comprehensive use of the advantages of both and designs the path 518 optimization problem based on Dijkstra's algorithm and the ant colony algorithm, 519 which overcome the difficulty of using the ant colony algorithm alone with a large 520 amount of computation. ...
This paper proposed an IoT-based intelligent logistics dispatching system, which enables dynamic coordination between customers, order-picking robots and cloud technology. This system includes three layouts: the framework structure of intelligent dispatching platform based on an IoT environment; the multi-objective optimization model to achieve the efficient dynamic coordination between customers, order-picking robots and the cloud technology; the core two-level algorithm, which comprises of Dijkstra’s algorithm and ant colony algorithm that supports the intelligent dispatching operations. This research shows its ability to efficiently coordinate the dispatching operations through IoT technology to enhance customer satisfaction, and outperforms the traditional dispatching methodologies.
... These algorithms are inspired from the nature and mainly they mimic the animal hunting behavior towards the food source. Some meta-heuristics approaches used for analog circuit design are particle swarm optimization [11], harmony search algorithm [12] and ant colony optimization [13]. These techniques try to provide the optimal solution to the problem. ...
A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works
... They are optimization algorithms based on natural decision-making mechanisms [1]. One of these algorithms is the ant colony optimization (ACO) algorithm [2]. This algorithm is inspired by the behavior of real ants; or rather it reduced to their ability to finding the shortest paths to a food source. ...
The article considers various graph creation methods for presenting the structure of a multiversion software package in solving its optimization problem by the ant colony optimization algorithm. The paper presents a method for calculating the weights of arcs for a static version of the graph and the reliability characteristics of the software package, taking into account its architecture. The version of creating a dynamic version of the graph is considered. A comparative analysis of these versions is conducted. The conclusions about the preferred application of the static version of the graph are made.
... It is inspired from the collaboration of a set of ants by using the pheromone (a chemical substance) and that is used to find the optimal path in a graph. This method has been applied to a wide range of discrete optimization problems (see [Dorigo 1999]). ...
Knowledge extraction from data bases, also called data mining, denotes theprocess of discovering useful, new and understandable knowledge from largedata bases. Among data mining tasks where the nature of information extractionprocess may be seen as an optimization process. Consequently, we have neededfundamental approach different conventional approaches of knowledge discovery.This approach is based on inspiration, ideas and insights from the natural life tosolve the knowledge extraction problems. In this thesis, we propose two novelbio-inspired algorithms to solve discrete optimization problems.The first algorithm is called Quantum inspired Differential Evolution with ParticleSwarm Optimization ‘QDEPSO’ which combines differential evolution, particleswarm optimization method and quantum-inspired evolutionary algorithm . Inthe initialization phase, the QDEPSO uses the concepts of quantum computing asthe superposition state of qubits as well as the quantum measurement to representand generate the diversity of the initial solutions. The second phase is an alter-nation between the DE operations and the adaptation of update formula of thevelocity and the position of PSO algorithm.The second algorithm is called Quantum-inspired Firefly Algorithm with ParticleSwarm Optimization ‘QIFAPSO’. This proposed algorithm uses the basic conceptsof quantum computing such as superposition states of Q-bit and quantum mea-sure to ensure a better control of the solutions diversity. Finally, the ‘QIFAPSO’combine two strategies that cooperate in exploring the search space: the first oneis the move of less bright fireflies towards the brighter ones and the second strat-egy is the PSO movement in which a firefly moves by taking into account its bestposition as well as the best position of its neighborhood. Finally, we propose theused QIFAPSO based on rough set for feature selection in classification.
... The fruit fly optimization algorithm is a swarm intelligence algorithm, similar to the ant colony optimization algorithm (ACO) [26,27], particle swarm optimization algorithm (PSO) [28], and others inspired by the foraging behavior of groups. The fruit fly may be more sensitive than other species, especially their senses of smell and vision. ...
Fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.
... These algorithms are inspired from the nature and mainly they mimic the animal hunting behavior towards the food source. Some meta-heuristics approaches used for analog circuit design are particle swarm optimization [11], harmony search algorithm [12] and ant colony optimization [13]. These techniques try to provide the optimal solution to the problem. ...
A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works
... ACO is one of the most practical multiagent systems. ACO has been applied to solving a traveling salesman problem or network routing problem [32,33]. ACO is a kind of metaheuristics that is used to obtain quasioptimal solutions independently of specific computational conditions. ...
Günümüzde enerji tüketiminin giderek arttığı, çevre koruma bilincinin geliştiği ve enerji piyasalarındaki serbestleşmenin istikrarlı şekilde ilerlediği görülmektedir. Bu nedenle, yeni teknolojiler yardımı ile yenilenebilir enerji kaynaklarından (YEK) daha fazla yararlanmaya yönelik politikalar üretilmekte ve dağıtılmış üretim sistemlerine olan ilginin artması sağlanmaktadır. Mikro şebekeler YEK’lerin şebekeye entegrasyonunda önemli avantajlar sağlayan bir çözüm olarak ortaya çıkmıştır. Mikro şebekelerin yapılarında bulunan YEK’lerin değişken üretim yapıları gibi bazı belirsizliklere sahip olması, bu sistemlerde frekans kararlılığını önemli problemlerden biri haline getirir. Bir mikro şebeke modeline etkin bir yük frekansı kontrolünün (YFK) sağlanması, bu sistemlerin kararlılığı ile ilgili iyileştirmelere önemli ölçüde katkıda bulunur. Bu çalışmada, mikro şebekede, YFK’de karşılaşılan zorlukların üstesinden gelmek için bir fuzzy oransal integral türev(PID - Proportional-Integral-Derivative) kontrolör yapısı önerilmiştir. En uygun kontrolör parametrelerinin belirlenmesi için lig şampiyonası algoritması (LŞA), karınca koloni algoritması (KKA) ve parçacık sürü optimizasyon (PSO) algoritmaları kullanılmıştır. Aynı zamanda, fuzzy PID kontrolörünün kazançlarının en uygun değerlerinin elde edilmesi için zaman ağırlıklı mutlak hatanın toplamı (ITAE - Integral Time-weighted Absolute Error) maliyet fonksiyonu kullanılmıştır. Çalışma sonucunda önerilen kontrolörün performansı klasik PID kontrolör ile karşılaştırılmıştır.
The Big Bang-Big Crunch (BB–BC) method developed by Erol and Eksin (Adv Eng Softw 37:106–111, 2006 [1]) consists of two phases: a Big Bang phase, and a Big Crunch phase. In the Big Bang phase, candidate solutions are randomly distributed over the search space.
Neste trabalho, um modelo de programação não linear inteiro misto (PNLIM) para o problema de planejamento da expansão da rede de transmissão estático CA (PERTECA) é apresentado, considerando o desligamento ótimo de linhas de transmissão (DOLT). Considera-se também como referência o modelo de programação linear inteiro misto (PLIM) para o problema de planejamento da expansão da rede de transmissão estático CC (PERTECC) considerando DOLT, o qual tem sido chamado de "modelo generalizado". Adicionalmente é introduzido um modelo de planejamento da expansão da rede de transmissão multi-estágio CC (PERTMCC) considerando DOLT. Os modelos de PLIM e PNLIM foram implementados usando a linguagem de modelagem algébrico AMPL e solucionados usando os solvers comerciais CPLEX e KNITRO, respectivamente. Os sistemas testes: Garver de 6 barras, IEEE de 24 barras e IEEE de 118 barras; e os sistemas reais: Sul Brasileiro de 46 barras, Boliviano de 57 barras e Colombiano de 93 barras foram usados para avaliar os modelos. Para os sistemas de grande porte, ou reais, uma estratégia de redução do espaço combinatório de busca do problema é apresentada para reduzir o esforço computacional e, portanto, o tempo computacional necessário para encontrar uma solução ótima.
Physical layer impairments (PLIs) is a matter of great concern in optical networks to achieve the optimum performance. In order to mitigate the PLI, a better routing and wavelength assignment (RWA) technique is required in optical wavelength division multiplexing (WDM) networks. In this paper, a dynamic ant colony routing algorithm with the consideration of various PLIs has been presented. This work also presents a Q-factor estimation model that includes impairments such as amplified spontaneous emission (ASE) noise, four-wave mixing (FWM), and cross-phase modulation (XPM). The simulation results also prove that the proposed ant colony optimization (ACO) based algorithm significantly reduces the blocking probability as compared to dynamic load based shortest path algorithm (LB-SPA).
Ant colony method was successfully used for calculating the electric power system reliability indices. A multi-state system consisting of elements combined into a series-parallel structure is considered. When the system consists of elements with different reliability and performance (productivity or capacity) characteristics, its availability, defined as the ability to meet a demand, strongly depends on the selection of components and the system structure design. In this paper, we present which parameters affect the ant colony method by Taguchi method and their percentage of participation.
Nowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high‐performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state‐of‐the‐art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate‐in‐nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO‐CLA is proposed to solve task scheduling problem in the mesh‐topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO‐CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second‐best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO‐CLA is 2.66% and 5.15% better than the ETF (the second‐best approach) and the average performance of all the other competitors.
This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.
A research on VLSI Floor planning’s physical layout is addressed using optimization methods to improve VLSI chip efficiency. VLSI floor planning is regarded as a non-polynomial issue. Calculations can solve such issues. Representation of floorplan is the basis of this process. The depictions of the floor plan demonstrate more effect on search space as well as the design complexity of the floor plan. This article aims at exploring various algorithms which add to the issue of managing alignment limitations such as excellent positioning, optimal region and brief run time. Many scientists are proposing and suggesting diverse heuristic algorithms and also distinct metaheuristic algorithms to solve the VLSI Floor plan issue. Simulated Annealing, tab search, ant colony optimization algorithm at last the genetic optimization algorithm are addressed in this article.
The e-mail foldering problem is a special classification problem. It concerns a situation where e-mail users create new folders and, at the same time, stop using some of the folders created in the past. Additionally, messages arrive in the system at different time stamps. This article proposes a novel approach to ant colony optimization adapted to data stream analysis. The article is related to the revision of the ant colony optimization algorithm in the e-mail foldering problem and the proposition of a new solution adapted to the data stream. The goal of this work is to allow the classification of messages arriving at the system as data packages; however, due to the large number of decision classes (folders in the inbox), successive packages lead to a large concept drift. To assure the stability of the algorithm, an approach based on the memory being represented as a pheromone trail is introduced. This concept is known from the ant colony optimization methods. At the same time, multiple numbers of classifiers (similar to an ensemble method) are included. The proposed approach was tested on real-world data from the Enron e-mail dataset. An analysis of the two proposed methods related to the data stream was proposed. Both methods were compared with the methods used in the literature. The results achieved, in terms of the accuracy as well as the stability, confirm that (according to a statistical analysis) the proposed solutions are capable of better classifying e-mail messages derived from the system as data packages.
This paper considers the problem of scheduling 'n' jobs on three machine flow shop with transportation times between the machines to minimize the maximum completion time. This problem is known as NP-hard. Theoretically, results concerning optimal for two special cases of the problem are given. Special attention is also given to branch and bound (BAB) and local search methods. The BAB algorithms use the quickly computed but possibly rather weak lower bounds obtained from relaxation of machines capacity constraints. The (BAB) algorithms are then tested on a large set of test problems. Also, we develop, compare and test different local search methods (Descent, Simulation Annealing, Threshold Accepting, Tabu Search, Genetic Algorithm, Ant Colony Algorithm) for the problem. Computational experience is found that these local search algorithms solve the problem to 7000 jobs with reasonable time.
Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.
Abstract Combinatorial optimization challenges are rooted
in real-life problems, continuous optimization problems, dis-
crete optimization problems and other signi�cant problems in
telecommunications which include, for example, routing, de-
sign of communication networks and load balancing. Load
balancing applies to distributed systems and is used for man-
aging web clusters. It allows to forward the load between
web servers, using several scheduling algorithms. The main
motivation for the study is the fact that combinatorial op-
timization problems can be solved by applying optimization
algorithms. These algorithms include ant colony optimiza-
tion (ACO), honey bee (HB) and multi-objective optimization
(MOO). ACO and HB algorithms are inspired by the foraging
behavior of ants and bees which use the process to locate and
gather food. However, these two algorithms have been sug-
gested to handle optimization problems with a single-objective.
In this context, ACO and HB have to be adjusted to multi-
objective optimization problems. This paper provides a sum-
mary of the surveyed optimization algorithms and discusses
the adaptations of these three algorithms. This is pursued by
a detailed analysis and a comparison of three major schedul-
ing techniques mentioned above, as well as three other, new
algorithms (resulting from the combination of the aforemen-
tioned techniques) used to e�ciently handle load balancing
issues.
A Metaheuristic optimization method can be considered as a more comprehensive intuitive method for the solution of optimization problems. Metaheuristic Optimization Methods developed in recent years can also be considered as metaphor‐based optimization methods. All the metaheuristic methods are based on the use of random numbers (probabilistic approaches) in the various stages of the optimization process. Whereas the convergence of the methods such as genetic algorithms, simulated annealing and ant colony optimization has been established, the convergence of most of the recently developed metaheuristic optimization methods does not appear to have been established at the present time. In fact, some researchers think that although the names of the methods might be different, the fundamental ideas used are the same in most of the recently developed metaheuristic optimization methods. The chapter lists the metaphors associated with several of the recently developed metaheuristic optimization methods.
The paper discusses the use of an optimization algorithm based on the behaviour of the ant colony to solve the problem of forming the composition of a multiversion fault-tolerant software package. A model for constructing a graph for the implementation of the ant algorithm for the selected task is proposed. The modifications of the basic algorithm for both the ascending and the descending design styles of software systems are given. When optimizing for downstream design, cost, reliability, and evaluation of the successful implementation of each version with the specified characteristics are taken into account. When optimizing for up-stream design, reliability and resource intensity indicators are taken into account, as there is a selection from already implemented software modules. A method is proposed for increasing the efficiency of the ant algorithm, which consists in launching a group of “test” ants, choosing the best solution from this group and further calculating on the basis of it. A software system that implements both modifications of the basic ant algorithm for both design styles, as well as the possibility of applying the proposed multiple start technique to both modifications, is considered. The results of calculations obtained using the proposed software tool are considered. The results confirm the applicability of ant algorithms to the problem of forming a multiversion software package, and show the effectiveness of the proposed method.
We discuss the simulation of M replications of a uniformizable Markov chain simultaneously and in parallel (the so-called parallel replicated approach). Distributed implementation on a number of processors and parallel SIMD implementation on massively parallel computers are described. We investigate various ways of inducing correlation across replications in order to reduce the variance of estimators obtained from the M replications. In particular, we consider the adaptation of Stratified Sampling, Latin Hypercube Sampling, and Rotation Sampling to our setting. These algorithms can be used in conjunction with the Standard Clock simulation of uniformized chains at distinct parameter values and can potentially sharpen multiple comparisons of systems in that setting. Our investigation is primarily motivated by this consideration. 1 Introduction Parallel/distributed approaches for discrete event simulation may be categorized as singlerun and multiple-run methods. The majority of studies ...
this paper continue to hold when the lag Delta is not held constant, but is allowed to vary from one ant to the next. We could also allow for the chasing ant to be guided by an ant other than the one immediately ahead. To achieve the asymptotic results, we need only ensure that eventually the current ant is many generations removed from the first one. Also we need to have Delta 2 infinitely often at each stage of the walk. The results discussed in this paper can be generalized to three (or more) dimensional space. The probability of A n+1 moving along each axis will, in this case, be proportional to the projection of the vector A n Gamma A n+1 along this axis. Ants obeying the probabilistic pursuit model have the property of moving, on the average, in the same direction as a continuous pursuit. However, their speed is not constant since it depends on the location of the chaser relative to the target. To overcome this problem, for purposes of approximating continuous pursuit, one might consider the following Euclidean probabilistic rule of pursuit:
We study how to improve the throughput of high-bandwidth traffic such as large file transfers in a network where resources are fairly shared among connections. While it is possible to devise priority or reservation-based schemes that give high-bandwidth traffic preferential treatment at the expense of other connections, we focus on the use of routing algorithms that improve resource allocation while maintaining max-min fair share semantics. In our approach, routing is closely coupled with congestion control in the sense that congestion information, such as the rates allocated to existing connections, is used by the routing algorithm. To reduce the amount of routing information that must be distributed, an abstraction of the congestion information is introduced. Using an extensive set of simulation, we identify a link-cost or cost metric for "shortest-path" routing that performs uniformly better than the minimal-hop routing and shortest-widest path routing algorithms. To further improve...
We propose a learning algorithm for solving the traveling salesman problem based on a simple strategy of trial and adaptation: i) A tour is selected by choosing cities probabilistically according to the ``synaptic'' strengths between cities. ii) The ``synaptic'' strengths of the links that form the tour are then enhanced (reduced) if the tour length is shorter (longer) than the average result of the previous trials. We perform extensive simulations of the random distance traveling-salesman problem. For sufficiently slow learning rates, near optimal tours can be obtained with the average optimal tour lengths close to the lower bounds for the shortest tour lengths. Comment: 10 pages, Latex, 3 postscript figures