基于均匀设计的聚类多目标粒子群优化算法

为更有效地求解多目标优化问题，提出一种基于均匀设计的聚类多目标粒子群算法UCMOPSO。采用基于均匀设计的交叉操作尽可能地获得目标空间中均匀分布的非劣解，帮助种群跳出局部最优解，并通过一种新的聚类操作选择外部存档中有代表性的非劣解，从而控制外部存档规模，降低计算复杂度。对基准函数的测试结果表明，UCMOPSO算法相比同类算法在收敛性和分布性方面具有优势。

Clustering Multi-objective Particle Swarm Optimization Algorithm Based on Uniform Design

In order to solve multi-objective problems efficiently,this paper proposes a clustering multi-objective Particle Swarm Optimization（PSO） algorithm based on uniform design named UCMOPSO.Crossover operation based on uniform design is adjusted to get uniformly distributed solutions in objective space to help swarm to escape from local optima,and a new clustering operator is introduced to select the representative non-dominated solutions,which decreases the computation complexity and limits the size of the external archive.Experimental results based on benchmark functions indicate that UCMOPSO has superiority in convergence and distribution compared with other algorithms.