BSO20: efficient brain storm optimization for real-parameter numerical optimization

Abstract

Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.

DBCC2: an improved difficulty-based cooperative co-evolution for many-modal optimization

“…In this section, we compare the differences in the behavior of DBCC2-DE, DBCC2-PSO, and DBCC2-CSA by convergence analysis. In global optimization, the radius of the population is generally used to analyze the convergence trend of the population [21,64]. However, considering that the population is divided into multiple species by the niching strategy in multimodal optimization, the mean of the radius of each species is used to analyze the convergence trend of the population in this paper.…”

An improved brain storm optimization with chunking‐grouping method

“…Chen et al 20 proposed a role‐playing strategy (RPBSO) inspired by corporate decision making processes. In BSO20, 29 a NBC‐RGS hybrid clustering strategy is designed, in which NBC (nearest‐better clustering) is used to cluster better individuals while RGS (random grouping strategy) is used to cluster other individuals. A max‐fitness clustering approach 21 is presented to replace the k‐means clustering method, in which the individual with best fitness is viewed as cluster center, then the individuals with nearest Euclidean distance are grouped into the cluster, this operation is repeated until all individuals find a cluster center. Individual updating strategy: Many individual updating strategies have been proposed.…”

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

“…In past years, BSO and its various variants have been widely developed to settle all kinds of complex and intractable optimization problems. Xu et al [35] introduced an improved BSO to handle a real-parameter numerical optimization problem. Cheng et al [36] proposed a modified BSO for solving a knowledge spillover problem.…”

“…The original BSO is straightforward in its design and can be easily implemented. Over the past years, BSO and its derivatives have achieved impressive results in tackling a range of complex problems, including real-parameter numerical optimization, distributed flow shops, and knowledge spillover problems [35][36][37][38][39][40]. Comprehensive experiments have verified that BSO possesses powerful performance to provide an outstanding compromise between exploration and exploitation abilities.…”