ArticlePublisher preview available

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

Authors:
Tanmay Kundu at Manipal Academy of Higher Education
Tanmay Kundu
Deepmala -- at PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur
Deepmala --
Pramod K. Jain
Pramod K. Jain
Pramod K. Jain
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.
Figures - available from: Applied Intelligence
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Applied Intelligence
This content is subject to copyright. Terms and conditions apply.
https://doi.org/10.1007/s10489-021-02862-w
A hybrid salp swarm algorithm based on TLBO for reliability
redundancy allocation problems
Tanmay Kundu1·Deepmala1·Pramod K. Jain2
Accepted: 17 September 2021/ Published online: 10 February 2022
©The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Abstract
A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO)
is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints.
Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps.
It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems.
However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting
in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed
where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA
and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component
reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark
problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system,
overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38,
40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed
competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results
are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the
results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a
remarkable and promising tool for solving RRAP.
Keywords Salp swarm algorithm ·TLBO ·Reliability redundancy allocation problem ·Constrained optimization
1 Introduction
Since 1950, reliability optimization plays a progressively
decisive role because of its critical requirements on sev-
eral engineering and industrial applications, and has become
a hot research topic in the engineering field. To be more
competitive in daily life, the basic goal of a reliability
engineer is always to improve the reliability of product com-
ponents or manufacturing systems. Obviously, an excellent
reliability design facilitates a system to run more safely
and reliably. In general, reliability optimization problems
Deepmala
dmrai23@gmail.com
Extended author information available on the last page of the article.
can be classified into two classes: integer reliability prob-
lems (IRP) and mixed-integer reliability problems (MIRP).
In IRP, the components reliability of the system is known
and the main task is only to allocate the redundant com-
ponents number. In case of MIRP, both the component
reliability and the redundancy allocation of the system are
to be designed simultaneously. This kind of problem in
which the reliability of the system is maximized through
the choices of redundancy and component reliability is
also known as the reliability-redundancy allocation prob-
lem (RRAP). To optimize a RRAP, redundancy levels and
component reliabilities of the system are considered as inte-
ger values and continuous values lies between zero and
one respectively. Several researchers works on this field to
solve RRAP with the objective of maximizing system reli-
ability under constraints such as the system cost, volume,
and weight etc., [44–48,66,76]. RRAP has been consid-
ered to be an NP-hard combinatorial optimization problem
because of its complexity and it has been considered as
Applied Intelligence (2022) 52:12630–12667
1 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Most recently in 2022, Kundu et. al., used a hybrid salp swarm algorithm (HSS-TLBO) based on teachinglearning based optimization (TLBO) for RRAPs, aiming to merge the SSA ability of global search with the fast converging of (TLBO) to maximize reliability [16]. ...
... Problem (P1) symbolizes the series-parallel system depicted in Fig. 4; the problem is mathematically formulated as in Eq. (15). Table 2 shows the test input data [2], the (w5) value differs according to [16] as specified in Table 2, this is to be considered in the comparisons. For problem P2, the complex/bridge system is illustrated in Fig. 5, the formulation of the problem is shown in Eq. (16). ...
... Table 2 shows the test input data [2], the (w5) value differs according to [16] as specified in Table 2, this is to be considered in the comparisons. For problem P2, the complex/bridge system is illustrated in Fig. 5, the formulation of the problem is shown in Eq. (16). The input data used for the test are given in Table 3 [2]. ...
The Exploration of Wild Horse Optimization in Reliability Redundancy Allocation Problems
Article
Full-text available
  • Aug 2022
In this work, the wild horse optimization (WHO) algorithm, known for its ease of use, efficiency, and fast convergence, is explored in solving the reliability redundancy allocation problem (RRAP) for series-parallel systems. This problem has as of late caught the attention of researchers in this area, especially in today’s rapidly growing field of artificial intelligence. The NP-hard RRAP problem deals with maximizing of reliability under certain constraints. This work uses WHO algorithm to maximize the overall system reliability by determining how many redundant components are to be used along with their reliabilities in each subsystem, such reliability is constrained by cost, volume, and weight. Testing is carried out to show the effectiveness of this algorithm using four known numerical examples, results are to be compared with simplified swarm algorithm (SSO), attraction-repulsion imperialist competitive algorithm (AR-ICA), hybrid salp swarm algorithm and teaching-learning based optimization (HSSTLBO), particle swarm optimization (PSO), and gradient based optimization (GBO). Computational results show that WHO was able to find better feasible near-optimal solutions effectively and efficiently in terms of population size and number of iterations. © 2022. International Journal of Intelligent Engineering and Systems.All Rights Reserved
View
... • Wilcoxon signed-rank test. Wilcoxon signed-rank test is often employed for comparing the significant difference between two optimization algorithms (Bagheri Tolabi et al. 2021;Kumar and Singh 2021;Kundu et al. 2022;Li et al. 2022). Compared with the t-test, Wilcoxon signed-rank test has the following three advantages (Derrac et al. 2011): (1) Wilcoxon signed-rank test is more sensitive than the t-test; ...
Backtracking search algorithm driven by generalized mean position for numerical and industrial engineering problems
Article
Full-text available
  • Apr 2023
  • ARTIF INTELL REV
Backtracking search algorithm (BSA) is a very popular and efficient population-based optimization technique. BSA has a very simple structure and good global search ability. However, BSA may be trapped into the local optimum in solving challenging multimodal optimization problems due to the single learning strategy. To enhance the global search ability of BSA, this paper proposes an improved version of BSA called backtracking search algorithm driven by generalized mean position (GMPBSA). In GMPBSA, two types of generalized mean positions are defined based on the built feature zones, which are employed to design the comprehensive learning mechanism consisting of three candidate learning strategies. Note that, this learning mechanism doesn’t introduce new control parameters and refer to the complex calculation. To verify the performance of GMPBSA, GMPBSA is used to solve the well-known CEC 2013 and CEC 2017 test suites, and three complex engineering optimization problems. Experimental results support the great potential of GMPBSA applied to the challenging multimodal optimization problems. The source code of GMPBSA can be found from https://github.com/jsuzyy/GMPBSA.
View
... Therefore, scholars commonly use many strategies to strengthen the SSA [62][63][64][65]. In recent years, SSA and its improved variants have manifested their capability in solving various optimization problems and many real-life applications [66][67][68][69][70][71][72][73][74] and exhibit outstanding performance in the domain of image segmentation [62,[75][76][77][78]. Nonetheless, these improved SSA variants still have some drawbacks of improper balance between exploitation and exploration phases and lack of population diversity. ...
Salp swarm algorithm with iterative mapping and local escaping for multi-level threshold image segmentation: A skin cancer dermoscopic case study
Article
Full-text available
  • Jan 2023
If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm method (ILSSA) that combines iterative mapping (IM) and local escaping operator (LEO) to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses 2D Kapur's entropy as the objective function and employs non-local means (NLM) 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.
View
... Newly enhanced algorithms include the fractional-order modified Harris hawks optimizer (FMHHO) 26 , the modified manta ray foraging optimization algorithm (MMRFOA) 27 , an enhanced slime mould algorithm 28 , the hybrid marine predator algorithm (HMPA) 29 , partitioned step particle swarm optimization (PSPSO) 30 , the improved chimp optimization algorithm (ICHOA) 31 , the high performance cuckoo search algorithm (HPCSA) 32 , the comprehensive learning marine predator algorithm (CLMPA) 33 , the enhanced sparrow search algorithm (ESSA) 34 , the hybrid algorithm that is known as three-learning strategy PSO (TLS-PSO) 35 , the enhanced shuffled shepherd optimization algorithm (ESSOA) 36 , the hybrid salp swarm algorithm with teaching-learning-based optimization (HSSATLBO) 37 , and an enhanced hybrid of crisscross optimization and the arithmetic optimization algorithm (CSOAOA) 38 . Both original and enhanced metaheuristic optimization algorithms are used in a wide range of fields, including engineering, business, transportation, energy, and even the social sciences. ...
Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems
Article
Full-text available
  • Nov 2022
The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.
View
... Recently, Kundu et. al. [59] proposed a hybrid salp swarm algorithm with teaching-learning-based optimization (HSSATLBO) for solving RRAP. ...
INNA: An improved neural network algorithm for solving reliability optimization problems
Article
Full-text available
  • Aug 2022
  • NEURAL COMPUT APPL
The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.
View
Quantized Orthogonal Experimentation SSA (QOX-SSA): A Hybrid Technique for Feature Selection (FS) and Neural Network Training
Article
  • May 2024
The standard metaheuristics are commonly based on a single metaphorical model miming a particular animal group's food-searching behaviour. As a result, the contributions of such approaches are becoming inadequate in dealing with the evolving complexities of optimizing objective functions. Salp Swarm Algorithm (SSA) is one of them, a new swarm intelligence-based technique relying on a lone metaphor introduced for tackling global optimization issues. Nonetheless, SSA has garnered substantial acknowledgement and attraction among the research community because it is easy to implement and requires few control parameters to fine-tune. However, the typical SSA encounters confinement issues in local optima and an insufficient convergence pace when confronted with more intricate scenarios due to deficient population diversity, local exploitation, and global exploration. Therefore, this research integrates a Quantized Orthogonal Experimentation (QOX) operator to enhance population variety and intensify SSA's local exploitation and global explorative potential. The resulting hybrid approach is named QOX-SSA. QOX-SSA's optimization skill is demonstrated using 14 fundamental and 30 advanced benchmark problems of IEEE-CEC-2014 and comparing its effectiveness to some contemporary metaheuristics. Three nonparametric tests are conducted to verify the statistical importance of QOX-SSA's results. Furthermore, the applicability of QOX-SSA is examined by implementing it to train the Radial Basis Function Neural Network to classify data and resolve problems related to optimal feature selection. Experimental outcomes of QOX-SSA over different optimization issues confirm its superior performance compared to SSA and the alternate metaheuristics used for comparative analysis.
View
Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)
Preprint
Full-text available
  • Mar 2024
Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The OMPCDPSO algorithm used many parts of the best solution. It put them in the multi-parent crossover and neighborhood search with onlookers. This made it better than DPSO. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.
View
A New Hybrid Particle Swarm Optimization–Teaching–Learning-Based Optimization for Solving Optimization Problems
Article
Full-text available
  • Dec 2023
This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of “exploitation capabilities of PSO” is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, “exploration abilities of TLBO” means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.
View
View
A Dual-Strategy Contest Harmony Search Algorithm Based on Population State for the System Reliability-Redundancy Allocation Problems
Chapter
  • Jul 2023
At present, many improved algorithms have effectively improved the ability to converge to the optimal value. However, there are still difficulties: how to effectively balance the deep development ability of the algorithm in a small range and the extensive exploration ability in the global search field in the iterative process. For this reason, this topic first proposes a population state evaluation indicator, which continuously detects the state and diversity of the population with iteration. On this basis, the dual-strategy competition mechanism is adopted. Through the population state evaluation index, the strategy can select a strategy that is more conducive to rapid convergence according to the changes of the population during the iteration process. Through the advantage competition of the two strategies, the development ability of the algorithm has been steadily and persistently improved. However, there is still the risk of falling into the local optimal dilemma. Therefore, the change of population diversity is considered in the evaluation index of population status in this study. In order to further increase the activity of the global search range, the computational individuals used in the proposed environmental assessment index of population diversity are randomly selected. In general, in the greedy comparison of the two strategies, this study still ensures the consideration and influence of diversity, and effectively balances the ability of algorithm exploration and development.KeywordsHarmony Search AlgorithmDual-strategy contest mechanismPopulation state evaluationDiversity evaluationSystem reliability
View
A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
Article
Full-text available
  • Apr 2021
In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and \(\beta \)-hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems.
View
Salp Swarm Optimizer for Modeling the Software Fault Prediction Problem
Article
Full-text available
  • Feb 2021
This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereafter. In order to find the optimal BPNN parameters, a combination of SSA optimizer and BPNN named (SSA-BPNN) is proposed, so as to enhance prediction accuracy. The proposed method is evaluated against several datasets for the SFP problem. These datasets vary in both size and complexity. The results obtained are evaluated using a variety of performance measures (i.e., the AUC, Confusion Matrix, Sensitivity, Specificity, Accuracy, and Error Rate). The results obtained by SSA-BPNN are better than those obtained by the conventional BPNN over all of the datasets. The proposed method also has the ability to outperform several state-of-the-art methods over the same datasets in respect of most of the aforementioned performance measures. Therefore, the hybridization of SSA with BPNN is a valuable addition to the software engineering issues and can be utilized to achieve higher prediction accuracy for a variety of prediction problems.
View
Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems
Article
Full-text available
  • Feb 2021
  • ENG COMPUT-GERMANY
Salp Swarm Algorithm (SSA) is a recent metaheuristic algorithm developed from the inspiration of salps’ swarming behavior and characterized by a simple search mechanism with few handling parameters. However, in solving complex optimization problems, the SSA may suffer from the slow convergence rate and a trend of falling into sub-optimal solutions. To overcome these shortcomings, in this study, versions of the SSA by employing Gaussian, Cauchy, and levy-flight mutation schemes are proposed. The Gaussian mutation is used to enhance neighborhood-informed ability. The Cauchy mutation is used to generate large steps of mutation to increase the global search ability. The levy-flight mutation is used to increase the randomness of salps during the search. These versions are tested on 23 standard benchmark problems using statistical and convergence curves investigations, and the best-performed optimizer is compared with some other state-of-the-art algorithms. The experiments demonstrate the impact of mutation schemes, especially Gaussian mutation, in boosting the exploitation and exploration abilities.
View
Coronavirus herd immunity optimizer (CHIO)
Article
Full-text available
  • May 2021
  • NEURAL COMPUT APPL
In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.
View
Slime mould algorithm: A new method for stochastic optimization
Article
Full-text available
  • Apr 2020
  • FUTURE GENER COMP SY
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the proposed SMA algorithm benefits from competitive, often outstanding performance on different search landscapes. Source codes of SMA are publicly available at https://www.aliasgharheidari.com/SMA.html
View
Improved salp swarm algorithm for feature selection
Article
Full-text available
  • Dec 2019
Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. This paper tries to improve the structure of basic SSA to enhance solution accuracy, reliability and convergence speed. A new control parameter, inertia weight, is added to adjust the present best solution. The new method known as improved salp swarm algorithm (ISSA) is tested in feature selection task. The ISSA algorithm is consolidated with the K-nearest neighbor classier for feature selection in which twenty-three UCI datasets are utilized to assess the performance of ISSA algorithm. The ISSA is compared with the basic SSA and four other swarm methods. The results demonstrated that the proposed method produced superior results than the other opti-mizers in terms of classification accuracy and feature reduction. Ó 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
View
Salp swarm algorithm: a comprehensive survey
Article
Full-text available
  • Aug 2020
  • NEURAL COMPUT APPL
This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.
View
A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean
Article
  • Jan 2021
This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.
View
A mayfly optimization algorithm
Article
  • May 2020
  • COMPUT IND ENG
This paper introduces a new method called the Mayfly Algorithm (MA) to solve optimization problems. Inspired from the flight behavior and the mating process of mayflies, the proposed algorithm combines major advantages of swarm intelligence and evolutionary algorithms. To evaluate the performance of the proposed algorithm, 38 mathematical benchmark functions, including 13 CEC2017 test functions, are employed and the results are compared to those of seven state of the art well-known metaheuristic optimization methods. The MA’s performance is also assessed through convergence behavior in multi-objective optimization as well as using a real-world discrete flow-shop scheduling problem. The comparison results demonstrate the superiority of the proposed method in terms of convergence rate and convergence speed. The processes of nuptial dance and random flight enhance the balance between algorithm’s exploration and exploitation properties and assist its escape from local optima.
View
Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Article
  • Dec 2019
  • EXPERT SYST APPL
Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.
View