Abstract:A new multiobjective particle swarm optimization (MOPSO) based on enhanced εdominance is proposed, which keeps good diversity. A new idea named guide mutation is introduced to select global guide from the archive. Then a population updating strategy and selfadaptive mutation operation are shown to speed up convergence. Experimental results show that the proposed approach has effective and steadystate performance and is simple to implement.
[1] Deb K, Agrawal S, Pratap A, et al. A Fast and Elitist Multiobjective Genetic Algorithm:NSGAII. IEEE Trans on Evolutionary Computation, 2002, 6(2): 182197 [2] Knowles J, Corne D. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation // Proc of the Congress on Evolutionary Computation. Mayflower Hotel, USA, 1999, Ⅰ: 98-105 [3] Hu X, Eberhart R. Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002: 1677-1681 [4] Zhang Libiao, Zhou Chunguang, Ma Ming, et al. Solutions of MultiObjective Optimization Problems Based on Particle Swarm Optimization. Journal of Computer Research and Development, 2004, 41(7): 12861291 (in Chinese) (张利彪,周春光,马 铭,等.基于粒子群算法求解多目标优化问题.计算机研究与发展, 2004, 41(7): 12861291) [5] Coello C C A, Pulido G T, Lechunga M S. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 256279 [6] SalazarLechuga M, Rowe J E. Particle Swarm Optimization and Fitness Sharing to Solve MultiObjective Optimization Problems // Proc of the IEEE Congress on Evolutionary Computation. Edinburgh, UK, 2005, Ⅱ: 12041211 [7] Laumanns M, Thiele L, Deb K, et al. On the Convergence and Diversity Preservation Properties of MultiObjective Evolutionary Algorithms. Technical Report, TIKReport 108, Zürich, Switzerland: Swiss Federal Institute of Technology. Computer Engineering and Communication Networks Laboratory, 2001 [8] Jiang Hao, Tang Huanrong, Zheng Jinhua. A Fast MultiObjective Genetic Algorithm Based on Quick Sort. Computer Engineering and Applications, 2005, 41(30): 4648 (in Chinese) (蒋 浩,唐欢容,郑金华.一种基于快速排序的快速多目标遗传算法.计算机工程与应用, 2005, 41(30): 4648) [9] Zheng Jinhua, Ling C X, Shi Zhongzhi, et al. Some Discussions about MOGAs: Individual Relations, Nondominated Set,and Application on Automatic Negotiation // Proc of the Congress on Evolutionary Computation. Portland, USA, 2004, Ⅰ: 706-712 [10] van Veldhuizen D A, Lamont G B. Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report, TR-98-03, Dayton, USA: Air Force Institute of Technology. Department of Electrical and Computer Engineering, 1998 [11] Schoot J R. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithms Optimization. Master Dissertation. Cambridge, USA: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, 1995