基于动态多种群的多目标粒子群算法

Multi-objective Particle Swarm Optimizer Based on Dynamic Multi-swarm

研究进化算法在求解多目标优化问题时,极易陷入到伪Pareto前沿(等价于单目标优化问题中的局部最优解),为了提高优化过程,提出一种基于动态多种群的多目标粒子群算法(DMSMOPSO)。在DMSMOPSO算法中,为了增加种群的多样性,提升粒子跳出局部最优解的能力,采用多子群进行搜索并且子群是动态地进行构建;采用K-均值聚类算法确定每个子群的搜索行为,提升种群向全局最优位置飞行的概率;根据目标函数的优化难度。通过典型的多目标测试函数和工程上的实际应用对算法进行仿真,仿真结果表明DMSMOPSO比其它算法优越,证明DMSMOPSO可作为求解多目标优化问题的有效算法。

In view of that multi-objective evolution algorithm easily converges to a false Pareto front,which is the equivalent of a local optimum in single objective optimization,a multi-objective particle swarm optimizer based on dynamic multi-swarm（DMSMOPSO for short） is discussed.In the DMSMOPSO algorithm,to increase the diversity of the swarm and improve the ability to escape from the local optima,the multi-swarm strategy is adopted to search for the feasible space.Also,the K-means clustering is used to confirm the center particle of each sub-swarm to guide the swarm flight,which will improve the probability of flying to Pareto front for the whole swarm.At last,in terms of the difficulty of different objective function,each sub-swarm assign different optimization task.Experiments were conducted on a set of classical benchmark functions and engineering application.Simulation results show that the proposed algorithm has better performance compared with other algorithms.