Zhao Yanchi, Cheng Jianhua, Cai Jing, Qi Bing
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China.
Beijing Institute of Space Long March Vehicle, Beijing, 100000, China.
Sci Rep. 2024 Mar 18;14(1):6432. doi: 10.1038/s41598-024-56919-0.
Recently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.
最近，为了解决头脑风暴优化（BSO）算法中收敛速度慢和优化精度低的问题，以下全局最优策略和讨论机制开始流行。然而，传统的BSO算法也存在容易陷入局部最优的问题。因此，这项工作创新性地设计了混沌差分步长策略。该策略引入了四种常用的混沌映射和差分步长来扩大种群搜索空间以改善这种情况。此外，基于对立学习的思想被创新性地应用于BSO算法中。该思想旨在生成基于对立的种群，增加搜索密度，并使算法尽快摆脱局部最优。总之，这项工作提出了一种基于混沌差分步长和基于对立学习的全局最优头脑风暴优化算法（COGBSO）。根据CEC2013基准测试套件，选择了15个典型的基准函数，并在MATLAB上进行了多组仿真实验。基于完整的CEC2018基准测试套件，还将COGBSO算法与最近的竞争算法进行了比较。结果表明，在解决复杂优化问题方面，COGBSO算法优于BSO算法和其他改进算法。
