SPBSO: self-adaptive brain storm optimization algorithm with p best guided step-size
Hu Peng, Changshou Deng, Zhijian Wu · Journal of Intelligent & Fuzzy Systems · 2019
As a new and promising swarm intelligence algorithm, brain storm optimization (BSO) has drawn more attention of researches and has been successfully applied to solve the real-world optimization problems. However, too many parameters make the algorithm more complex and greatly limit the convergence performance. Thus, this paper proposed a novel BSO variant, named self-adaptive BSO with p best guided step-size (SPBSO), in which a simple self-adaptive strategy is employed to choose a creating strategy in a random manner rather than depending on several adjustable parameters. In addition, the p best guided step-size and dynamic clustering number are used to accelerate the convergence speed. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2014 problems). Experimental results and comparison with the state-of-the-art BSO variants and some recently proposed PSO and DE algorithms, have proved that the proposed algorithm is competitive.