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.

Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems

“…(3a) is used to obtain the old idea based on one idea selected from one cluster, and (3b) is used to obtain the old idea based on two ideas selected from the two clusters. For the new solution generation operation, there are also a series of improvements to the original BSO, such as modified search step size [29], discussion mechanism [30], learning strategy [31], [32], etc. The selection strategy in BSO is to keep good solutions in all individuals.…”

Exploration Enhanced RPSO for Collaborative Multitarget Searching of Robotic Swarms

“…Intelligence (SI) algorithms were directly used to design the collaborative behaviors of robotic swarms. Besides the PSO algorithm, other SI algorithms can be found for robotic swarm applications in the literature, such as Brain Storm Optimization (BSO) [16][17][18] and its extension in the field of multirobots named Brain Strom Robotics (BSR) [19]: Bees Algorithm (BA) [20,21], Artificial Bee Colony (ABC) [22,23], Ant Colony Optimization (ACO) [24,25], Bacterial Foraging Optimization (BFO) [26,27], Glowworm Swarm Optimization (GSO) [28,29], Firefly Algorithm (FA) [30,31], and Grey Wolf Optimizer (GWO) [10,32], etc. e corresponding works are summarized in Table 2, which shows the original algorithm and related robotic applications correspondingly.…”