关键词: 多目标优化, 粒子群算法, 多策略, 邻域认知

Abstract: A new Multi-Objective Particle Swarm Optimization（MOPSO） algorithm is proposed based on multistrategy to avoid the problems about convergence rate of optimization algorithm and the non-inferior solution diversity cannot be satisfied simultaneously. It divides the external species and evolution population again by the non-dominated sorting and crowded distance ordering strategy. It chooses the best particle as a leader in the external populations to lead the evolution of the evolution of particles in the population by the niche selection strategy. In the evolutionary population, it balances the global searching and local accurate searching algorithm through the multi-scale Gaussian mutation strategy. It adopts the updating strategy of field cognitive individual extremum to update the extreme value of the individual constantly. The algorithm is applied to a typical multi-objective test function and compared to other multi-objective optimization algorithms. It is shown from the results that the four strategies of the algorithm are effective and complementary to each other and shown that the algorithm not only has good convergence and speed, but also has good uniformity and diversity at the same time.

Key words: multi-objective optimization, particle swarm optimization algorithm, multi-strategy, field of cognitive