Abstract:Pareto archive multiobjective particle swarm optimization (PAMOPSO) is designed, in which the improved method of strength Pareto evolutionary algorithm 2 (SPEA2) is used to maintain external archive and the global best location for each particle is selected in the procedure of archive maintenance. PAMOPSO is applied to five test instances and compared with other three multiobjective optimization algorithms. The computational results demonstrate that PAMOPSO has good performance in multiobjective optimization.
[1] Kennedy J, Eberhart R C. Particle Swarm Optimization. In: Proc of the IEEE International Conference on Neural Networks. Perth, Australia: IEEE Press, 1995, 1942-1948 [2] Hu X, Eberhart R, Shi Y H. Particle Swarm with Extended Memory for Multiobjective Optimization. In: Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003, 193-197 [3] Coello C C A, Pulido G T, Lechuga M S. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 256-279 [4] Knowles J D, Corne D W. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 2000, 8(2): 149-172 [5] Parsopoulos K E, Vrahatis M N. Particle Swarm Optimization Method in Multi-Objective Problems. In: Proc of the ACM Symposium on Applied Computing. Madrid, Spain, 2002, 603-607 [6] Schaffer J D. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proc of the 1st International Conference on Genetic Algorithm. Hillsdale, USA: Lawrence Erlbaum Associates, 1985, 93-100 [7] Mostaghim S, Teich J. Strategies for Finding Good Local Guides in Multi-Objective Particle Swarm Optimization. In: Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003, 26-33 [8] Hu X, Eberhart R C. Multi-Objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Proc of the Congress on Evolutionary Computation. Honolulu, USA, 2002, Ⅱ: 1677-1681 [9] Corne D W, Knowles J D. The Pareto Envelope-Based Selection Algorithm for Multi-Objective Optimization. In: Proc of the 6th International Conference on Parallel Problem Solving from Nature. Paris, France, 2000, 839-848 [10] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report, 103, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland, 2001 [11] Deb K, Agarwal S, Pratap A, Meyarivan T. A Fast and Elitist Multi-Objective Genetic Algorithms: NSGA-Ⅱ. IEEE Trans on Evolutionary Computation, 2002, 6(2): 182-197 [12] Deb K. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 1999, 7(3): 205-230 (廖小燕.基于定性映射的网络入侵检测系统的设计与实现. 硕士学位论文.上海海事大学,计算机系, 上海, 2004) [27] Wang L H, Feng J L, et al. Classifying and Learning Based on Qualitative Mapping. In : Proc of the International Conference on Intelligent Information Technology.Beijing, China, 2002, 559-563 [28] Zeki S. The Visual Image in Mind and Brain. Scientific American, 1992, 267(3): 68-76 [29] Qian X S. Developing Research about Noetic Science. In: Qian X S, ed. In Noetic Science. Shanghai, China: Shanghai People’s Press, 1986, 141 (in Chinese) (钱学森.开展思维科学的研究.见:钱学森,主编.关于思维科学.上海:上海人民出版社, 1986, 141)