No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
An Improved Method of Artificial Bee Colony Algorithm
Abstract
The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.
... Different types of bee algorithm have been proposed. In most existing bee algorithms known as honeybee-based algorithms, different forager bees are allocated to different nectar resources in order to maximize the obtained nectar (Pham et al. 2011;Rajasekhar et al. 2011;Akay and Karaboga 2012;Wu et al. 2012). Yang (2005) proposed the virtual bee algorithm in which pheromone intensity is directly linked with the objective function. ...
... While bee algorithm was originally designed for solving numerical problems (see Karaboga and Basturk 2007;Krishnanand et al. 2009;Wu et al. 2012), the bee algorithm has been utilized in broader applications in recent years (Karaboga et al. 2014). ...
Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative health, environment and economical implications. In recent times, computational intelligence (CI), a set of nature-inspired computational approaches and algorithms, has been used in vehicle routing and congestion mitigation research (also referred to as CI-based vehicle traffic routing systems—VTRSs). In this paper, we conduct a critique of existing literature on CI-based VTRSs and discuss identified limitations, evaluation process of existing approaches and research trends. We also identify potential research opportunities.
... many more researchers from different fields to study on ABC.Dongli et al. (2011); Akay and Karaboga (2012b) proposed a modified ABC algorithm for numerical optimization problems both of them are tested on a set of benchmark problems. Mezura-Montes et al. (2010) presented a novel ABC algorithm for solving constrained numerical optimization problems.Wu et al. (2011) described an improved ABC algorithm to enhance the global search ability of basic ABC.Li et al. (2012) used the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process and used this modified algorithm to numerical function optimization. ...
... The experimental results tested on six benchmark functions showed that GABC outperformed basic ABC. Wu et al. [13] described an improved ABC algorithm to enhance the global search ability of basic ABC. Guo et al. [14] presented a novel search strategy and the improved algorithm is called global ABC which has great advantages of convergence property and solution quality. ...
The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.
... The modification is often related to the ratio of variance operator and the frequency of perturbation. Wu et al. presented an improved ABC algorithm to enhance the global search ability of basic ABC [31]. ABC algorithm was later upgraded for constrained optimisation problems [32]. ...
Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.
... Dongli et al. (2011) proposed a modified ABC algorithm for numerical optimization problems and tested its performance on a set of benchmark problems. Wu et al. (2011d) described an improved ABC algorithm to enhance the global search ability of basic ABC. Rajasekhar et al. (2011b) proposed an improved version of ABC algorithm with mutation based on Levy probability distributions. ...
... At first, the ABC optimization approach was for solving continuous problems and its general features are detailed in [4]. Numerous researchers evaluate on the widely used set of numerical benchmark set functions and compare it with well-known metaheuristics [5]- [7]. Then, ABC algorithms were designed to handle combinatorial problems such as integer programming [8], binary optimization [9], flowshop scheduling [10], [11], project scheduling [12]- [14], travelling salesman [15], [16], portfolio optimization [17], [18], etc. ...
Cloud computing is the provision of computing resource services from which users can obtain resources via network to tackle their demands. In recent years, with fast growing information technology, more users apply this service; as a result, the demand has increased dramatically. In addition, most of the complex tasks are represented by workflow and executed in the cloud. Therefore, as service providers face this increasing demand, how to schedule the workflow and reduce the response time becomes a critical issue. This research integrates the concept of project scheduling with the workflow scheduling problem to formulate a mathematical model, which expects to minimize the total completion time. Two Artificial Bee Colony algorithms are proposed to solve the workflow scheduling optimization problem. The performance of ABC is compared with the optimal solutions obtained by Gurobi optimizer on the instance containing different sizes of workflows. The results show that ABC can be considered a practical method for complicated workflow scheduling problems in the cloud computing environment.
... Dongli et al. (2011) proposed a modified ABC algorithm for numerical optimization problems and tested its performance on a set of benchmark problems. Wu et al. (2011d) described an improved ABC algorithm to enhance the global search ability of basic ABC. Rajasekhar et al. (2011b) proposed an improved version of ABC algorithm with mutation based on Levy probability distributions. ...
Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its efficiency. However, the original ABC technique cannot be used in dynamic environments directly. This paper proposes a multiswarm ABC algorithm, or MABC, that has a similar framework as the original ABC but uses environment detecting technique to track the moving of the optimum of dynamic problems. Experiments have been carried out by comparing MABC with original ABC on moving peaks benchmark (MPB). Results show that the proposed MABC performs better in terms of the offline error, convergence speed, and robustness.
Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.
Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its easy of use and efficiency. However, the original ABC technique cannot be used in a binary search space directly. This paper proposes an effective binary ABC algorithm, or BitABC, that has a similar framework to the original ABC but uses a bitwise operation for the movements of the employed bees and onlooker bees. Experiments have been carried out by comparing BitABC with three binary ABC variants, DisABC, normABC, and BinABC, and the most used binary search technique, GA, on a set of 13 selected benchmark functions. Results show that the proposed BitABC performs better than, or at least comparable to, the other algorithms in terms of the final solution accuracy, convergence speed, and robustness.
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.
An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive
is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the
intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the
results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm
(PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
Aimed at the defects such as slow convergence and easy to fall into local optimization for standard ant colony algorithm to solve traveling salesman problem(TSP). Combinatorial optimization problem is transformed to searching farina for honey bees, based on the analysis of nectar searching, and artificial bee colony algorithm with three basic models is analyzed. Three regenerative strategies of leading gene are discussed. Dynamic renew formula of transforming gene and transferring formula of state are established and honeybee algorithm model. At last, concrete process of solving TSP by adopting ABC is proposed and some typical TSP samples are practiced. The results show that this algorithm can avoid pre-maturity and advance constringency and the algorithm has more advantages than ant standard colony algorithm.
AbstractArtificial Bee Colony (ABC) algorithm was firstly proposed for unconstrained optimization problems on where that ABC algorithm showed superior performance. This paper describes a modified ABC algorithm for constrained optimization problems and compares the performance of the modified ABC algorithm against those of state-of-the-art algorithms for a set of constrained test problems. For constraint handling, ABC algorithm uses Deb’s rules consisting of three simple heuristic rules and a probabilistic selection scheme for feasible solutions based on their fitness values and infeasible solutions based on their violation values. ABC algorithm is tested on thirteen well-known test problems and the results obtained are compared to those of the state-of-the-art algorithms and discussed. Moreover, a statistical parameter analysis of the modified ABC algorithm is conducted and appropriate values for each control parameter are obtained using analysis of the variance (ANOVA) and analysis of mean (ANOM) statistics.
Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.