An Adaptive Brain Storm Optimization Algorithm for Multiobjective Optimization Problems

Brain Storm Optimization (BSO) algorithm is a new swarm intelligence method that arising from the process of human beings problem-solving. It has been well validated and applied in solving the single objective problem. In order to extend the wide applications of BSO algorithm, a modified Self-adaptive Multiobjective Brain Storm Optimization (SMOBSO) algorithm is proposed in this paper. Instead of the

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k

-means clustering of the traditional algorithm, the algorithm adopts the simple clustering operation to increase the searching speed. At the same time, the open probability is introduced to avoid the algorithm trapping into local optimum, and an adaptive mutation method is used to give an uneven distribution on solutions. The proposed algorithm is tested on five benchmark functions; and the simulation results showed that the modified algorithm increase the diversity as well as the convergence successfully. The conclusions could be made that the SMOBSO algorithm is an effective BSO variant for multiobjective optimization problems.