Advanced discussion mechanism-based brain storm optimization algorithm

Abstract

Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems. We aim at designing advanced optimization algorithms, with inspiration from human's creative problem-solving strategies. In this paper, we proposed an advanced discussion mechanism-based brain storm optimization (ADMBSO) algorithm, pushing forward our study in the incorporation of inter- and intra-cluster discussions into the brain storm optimization algorithm (BSO) to control global and local searching ability, respectively. In the advanced discussion mechanism, elaborately designed inter- and intra-cluster discussions were alternatively performed throughout the optimization process, with the ratio controlled by a linearly adjusted probability. We further introduced a differential step strategy into the workflow, making ADMBSO a more efficient and more adaptive algorithm. Empirical studies on different function optimization problems illustrated the effectiveness and efficiency of the ADMBSO algorithm. Comparisons among the ADMBSO, BSO algorithm, closed-loop brain storm optimization algorithm, particle swarm optimization algorithm, and differential evolution algorithm, have also been provided in detail. As one of the first algorithms inspired by human behavior, ADMBSO demonstrates its great potential in dealing with complex optimization problems.