多子群协同进化的多目标微粒群优化算法

Multi-objective particle swarm optimization algorithm of multi-swarm co-evolution

微粒群优化(PSO)算法是一种非常有竞争力的求解多目标优化问题的群智能算法,因其容易陷入局部极值,导致非劣解集的收敛性和正确性不理想。为此提出一种基于多目标分解进化策略的多子群协同进化的多目标微粒群优化算法(MOPSO_MC),算法中每个子群对应于一个多目标分解之后的子问题,并构造了一种新的速率更新策略,每个粒子跟踪自身历史最优值、子群最优值和子群邻域最优值,从而在增强算法的局部寻优能力的同时,也能从邻域子群获得进化信息,实现协同进化。最后通过仿真实验,与现在主流的多目标微粒群算法在ZDT基准测试函数上比较,验证了算法的收敛性,解分布的均匀性和正确性。

Particle Swarm Optimization （PSO） algorithm is a very competitive swarm intelligence algorithm for multiobjective optimization problems. Because it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto set are not satisfactory, so the paper proposed a muhi-objective particle swarm optimization algorithm of multi-swarm coevolution based on decomposition （MOPSO_MC）. In the proposed algorithm, each sub-swarm corresponded to a sub-problem decomposed by multi-objective decomposition method, and the authors constructed a new update strategy for the velocity. Each particle followed its own previous best position, sub-swarm best position and sub-swarm neighborhood best position, which resulted in enhancing the ability of local searching and getting evolutionary information from the neighborhood sub-swarm. Finally, the simulation results verify the convergence of the proposed algorithm, and the uniformity and correctness of the solution distribution with comparison of the state-of-the-art multi-objective particle swarm algorithm on ZDT test function.