Nama Sukanta, Saha Apu Kumar
Department of Mathematics, MBB University, Agartala, Tripura India.
Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura India.
Cognit Comput. 2022;14(2):900-925. doi: 10.1007/s12559-021-09984-w. Epub 2022 Jan 30.
Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm's performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm's execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.
回溯搜索算法（BSA）是一种基于自然的优化技术，在过去几年中被广泛用于解决各种现实世界中的全局优化问题。当前的工作旨在引入一种基于多种群方法和改进控制参数设置的改进型回溯搜索算法（ImBSA），以理解各种变异策略的集合。在所提出的ImBSA中，提出了一种新的变异策略来提高算法的性能。此外，对于所有变异策略，控制参数在算法执行过程中自适应更新。在CEC2014和CEC2017单目标基准函数上进行了大量实验，并将结果与几种最新算法、改进的BSA变体、高效的差分进化（DE）变体、粒子群优化（PSO）变体以及其他一些混合变体进行了比较。进行了非参数Friedman秩检验以从统计学上检验所提出算法的效率。此外，还解决了六个实际工程设计问题以检验ImBSA的问题解决能力。实验结果、统计分析、收敛图、复杂度分析以及实际应用结果证实了所提出的ImBSA的卓越性能。
