Abstract
Intelligence algorithms play an increasingly important role in the field of intelligent control. Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by emulating the collective behavior of human beings in the problem solving process. To improve the performance of the original BSO, many variants of BSO are proposed. In this paper, an improved BSO algorithm with dynamic clustering strategy (BSO-DCS) is proposed as a variant of BSO for global optimization problems. The basic framework of BSO is firstly introduced. Then to reduce the time complexity of the original BSO, a new grouping method named dynamic clustering strategy (DCS) is proposed to improve the clustering method in the original BSO. To verify the effectiveness of the proposed BSO-DCS, it is tested on 12 benchmark functions of CEC 2005 with 30 dimensions. Experimental results show that DCS is an effective strategy to reduce the time complexity, and the improved BSO-DCS performs greatly better than the original BSO algorithm.
Highlights
Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by emulating the collective behavior of human beings in the problem solving process [10, 11]
After we firstly introduce the basic framework of BSO, in order to reduce the time complexity of the original BSO, a new grouping method named dynamic clustering strategy (DCS) is proposed to improve the clustering method in the original BSO
The experimental results show that the mean CPU times of BSO algorithm with dynamic clustering strategy (BSO-DCS) is lower significantly than the original BSO algorithm
Summary
In the past few decades, a lot of swarm intelligence optimization algorithms, such as particle swarm optimization (PSO) [1], ant colony optimization (ACO) [2], bee colony optimization (BCO) [3], firefly optimization algorithms (FFO) [4], bacterial forging optimization (BFO) [5], artificial raindrop algorithm (ARA) [6], have been proposed to tackle increasingly complex real-world optimization problems in the field of intelligent control [7,8,9]. Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by emulating the collective behavior of human beings in the problem solving process [10, 11]. An improved BSO algorithm with dynamic clustering strategy (BSO-DCS) is proposed as a variant of BSO for global optimization problems. After we firstly introduce the basic framework of BSO, in order to reduce the time complexity of the original BSO, a new grouping method named dynamic clustering strategy (DCS) is proposed to improve the clustering method in the original BSO. Similar to other swarm intelligence algorithms, BSO is a population-based stochastic optimization technique. M points of cluster center are randomly initialized like N ideas, where m is lesser than N. Like other evolutionary algorithms and swarm intelligence algorithms, the main operations will be divided into converging operation and diverging operation
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