BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

Abstract

Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems. However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations. To address these problems, in this paper, we propose a simple yet effective BSO variant named Brain Storm Optimization Algorithm with Cooperative Learning Strategy (BSO-CLS). It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators’ ideas. Thus, BSO-CLS iteratively updates the candidate solutions by linearly combining other solutions with the weights deriving from the fitness values of other solutions. To validate the effectiveness of the proposed method, we test it on 6 benchmark functions with the 1000 dimensions. The experimental results show that BSO-CLS can outperform the vanilla BSO and the other BSO variant with the learning strategy.