Abstract：Particle swarm optimization (PSO) is widely studied and applied due to its simple principle and easy implementation. Aiming at improving the convergence speed and the search precision, an algorithm based on PSO, team evolutionary algorithm (TeamEA), is presented.The optimization process of this algorithm is divided into two stages. At the first stage, to keep the diversity the players are divided into junior teams to optimize and the senior team is formed. At the second stage, to improve the convergence speed, only the senior team is optimized. In the process of the whole optimization, by evaluating the achievements of the players, the adjustment of step-length and the maximum space are controlled, and the coaching staff is formed to guide the progress direction of the players. Results on high-dimensional multimodal test functions validate the superiority and effectiveness of the proposed algorithm.
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2015, Vol. 28 
