BSO-AL: Brain Storm Optimization Algorithm with Adaptive Learning Strategy

Yang Shen, Jian Yang, Shi Cheng, Yuhui Shi

Abstract: Swarm intelligence algorithms have been widely and successfully used to optimize many science and engineering problems, the collective behavior of the agents lead to the emergence of intelligence. These interactions among agents can be classified into three categories: exploring, emulating and learning. Brain Storm Optimization (BSO) is a novel swarm intelligence algorithm which is inspired by the human brainstorming process, and generates new ideas by emulating existing ideas. In this paper, a new BSO algorithm with an adaptive learning strategy (BSOAL) is proposed. By considering the evolutionary speed factor of each individual and the aggregation degree of the swarm, the proposed BSO-AL generates new individuals by exploring, emulating or learning adaptively. Comparative experiments were conducted on a set of benchmark functions with different dimensions. The experimental results show that the proposed BSO-AL algorithm outperforms the classic BSO algorithm and the other two state-of-the-art algorithms, which demonstrates the effectiveness of the learning strategy.