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Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

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APPLIED MATHEMATICS AND COMPUTATION 2020年 370卷

作者： Hassan, Bryar A. Rashid, Tarik A. Kurdistan Inst Strateg Studies & Sci Res Sulaimani Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Comp Networks Sulaimani Iraq

backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems. This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA;therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are two-fold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and m

关键词： Swarm intelligence Evolutionary optimisation algorithms backtracking search optimisation algorithm optimisation problems BSA applications Performance evaluation

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Elliptical Antenna Array Synthesis Using backtracking search optimisation algorithm

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DEFENCE SCIENCE JOURNAL 2016年第3期66卷 272-277页

作者： Guney, Kerim Durmus, Ali Nuh Naci Yazgan Univ Fac Engn Dept Elect & Elect Engn TR-38170 Melikgazi Kayseri Turkey Erciyes Univ Vocat Coll Dept Elect & Energy TR-38039 Kayseri Turkey

The design of the elliptical antenna arrays is relatively new research area in the antenna array community. backtracking search optimisation algorithm (BSA) is employed for the synthesis of elliptical antenna arrays having different number of array elements. For this aim, BSA is used to calculate the optimum angular position and amplitude values of the array elements. BSA is a population-based iterative evolutionary algorithm. The remarkable properties of BSA are that it has a good optimisation performance, simple implementation structure, and few control parameters. The results of BSA are compared with those of self-adaptive differential evolution algorithm, firefly algorithm, biogeography based optimisation algorithm, and genetic algorithm. The results show that BSA can reach better solutions than the compared optimisation algorithms. Iterative performances of BSA are also compared with those of bacterial foraging algorithm and differential search algorithm.

关键词： Elliptical antenna array backtracking search optimisation algorithm sidelobe suppression

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Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms

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DATA IN BRIEF 2020年 28卷 105046-105046页

In this data article, we present the data used to evaluate the statistical success of the backtracking search optimisation algorithm (BSA) in comparison with the other four evolutionary optimisation algorithms. The data presented in this data article is related to the research article entitles `Operational Framework for Recent Advances in backtracking search optimisation algorithm: A Systematic Review and Performance Evaluation' [1]. Three statistical tests conducted on BSA compared to differential evolution algorithm (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly algorithm (FF). The tests are used to evaluate these mentioned algorithms and to determine which one could solve a specific optimisation problem concerning the statistical success of 16 benchmark problems taking several criteria into account. The criteria are initializing control parameters, dimension of the problems, their search space, and number of iterations needed to minimise a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimisation problem in terms of hardness and their cohort. In addition, all the three tests include necessary statistical measures (Mean: mean-solution, S.D.: standard-deviation of mean-solution, Best: the best solution, Worst: the worst solution, Exec. Time: mean runtime in seconds, No. of succeeds: number of successful minimisation, and No. of Failure: number of failed minimisation). (C) 2019 The Author(s). Published by Elsevier Inc.

关键词： backtracking search optimisation algorithm Statistical analysis BSA experimental data BSA performance evaluation

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