Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

• Main Authors: Zhenyu Song, Xuemei Yan, Lvxing Zhao, Luyi Fan, Cheng Tang, Junkai Ji
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Algorithms
• Subjects: brain-storm optimization; chaotic local search; adaptive mechanism
• Online Access: https://www.mdpi.com/1999-4893/14/8/239

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.