CAJ | 학술논문

用粒子群优化算法求解多目标问题容易陷入局部最优，为此本文提出了一种分组粒子群多目标优化算法。该算法将决策空间分成Q个子空间，每个子空间随机的分配N个粒子，这Q个粒子群分别在各自的空间进行独立搜索。为保证每个种群的搜索多样性和遍历性，用混沌序列对各组粒子位置进行初始化，同时对各组进行基于聚集距离的粒子择优进化。由典型多目标函数的优化实验结果表明，经过适当的分组，该算法能迅速逼近非劣最优解集，效果令人满意。
용입자군우화산법구해다목표문제용역함입국부최우，위차본문제출료일충분조입자군다목표우화산법。해산법장결책공간분성Q개자공간，매개자공간수궤적분배N개입자，저Q개입자군분별재각자적공간진행독립수색。위보증매개충군적수색다양성화편력성，용혼돈서렬대각조입자위치진행초시화，동시대각조진행기우취집거리적입자택우진화。유전형다목표함수적우화실험결과표명，경과괄당적분조，해산법능신속핍근비렬최우해집，효과령인만의。
In order to solve the problem that it is easily plunged into local optima to use particle swarm optimization ( PSO) al-gorithm for multi-objective problem, this paper proposes a divisional PSO algorithm, named MODPSO.This algorithm divide func-tion domain into Q subspaces, each subspace will be randomly allocated N particles.These Q particle swarm search independently in their own space respectively.In order to guarantee each species'diversity and ergodicity of searching, chaotic sequence and crowding distance is used to initiate individual position and select the best individual .By proper dividing, experimental results on several typical multi-objective function show that the algorithm can rapidly find the Pareto optimal which is quite satisfactory .