Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment

Abstract

As a novel swarm intelligence optimization algorithm, brain storm optimization (BSO) has its own unique capabilities in solving optimization problems. However, the performance of traditional BSO strategy in balancing exploitation and exploration is inadequate, which reduces the convergence performance of BSO. To overcome these problems, a multi-strategy BSO with dynamic parameters adjustment (MSBSO) is presented in this paper. In MSBSO, four competitive strategies based on improved individual selection rules are designed to adapt to different search scopes, thus obtaining more diverse and effective individuals. In addition, a simple adaptive parameter that can dynamically regulate search scopes is designed as the basis for selecting strategies. The proposed MSBSO algorithm and other state-of-the-art algorithms are tested on CEC 2013 benchmark functions and CEC 2015 large scale global optimization (LSGO) benchmark functions, and the experimental results prove that the MSBSO algorithm is more competitive than other related algorithms.