Abstract: The crowing distance proposed in NSGA-II is adopted to calculate the crowing degree of the nondominated solutions in the external archive in this paper.The globally optimal position of each particle is predicted according to tournament selection scheme，and each particle is then guided to a sparse region of nondominated solutions.As a result，it is helpful for an algorithm to find a better distribution of the nondominated solutions.Moreover，the adoption of the dynamic mutation operator is beneficial to avoid the premature convergence.Based on all these，a novel particle swarm optimization algorithm is proposed and the simulations are made.The results indicate the effectiveness of the proposed algorithm.

Key words: Particle Swarm Optimization（PSO）, crowding distance, tounament selection, dynamic mutation operator

摘要： 该算法通过引用NSGA-II中的拥挤距离，确定外部档案中非支配解的拥挤度，依据竞标赛选择方法选出每个粒子的全局最优位置，引导每个粒子向处于较稀松区域的非支配解搜索，提高了解的多样性。动态变异算子的引入，减缓了算法的收敛速度，增大了解的搜索区域，避免了算法早熟收敛或陷入局部最优。实验结果表明，算法CDMOPSO比NSGA-II具有更好的收敛性和维持种群多样性的能力。

关键词: 粒子群算法, 拥挤距离, 竞标赛选择方法, 动态变异算子