A Mixed Strategies Pareto Evolutionary Programming
DONG HongBin1,2, HUANG HouKuan1, HE Jun1, HOU Wei3 , MU ChengPo1
1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044 2.Department of Computer Science, Harbin Normal University, Harbin 150080 3.Department of Computer Science, Agricultural University of the Northeast, Harbin 150030
Abstract：A evolutionary approach to solve the multiobjective optimization problems, Mixed Strategies Pareto Evolutionary Programming (MSPEP), is presented. Based on the performance of mutation strategies, the mixed strategy distribution is dynamically adjusted. By combining the Pareto strength ranking procedure with the mixed mutation strategies, a new evolutionary algorithm is proposed. The proposed approach is compared with other evolutionary optimization techniques in several benchmark functions. Experimental results demonstrate that the proposed method could rapidly converge to the Pareto optimal front and spread widely along the front.
[1] Srinivas N, Deb K. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 1994, 2(3): 221-248 [2] Zitzler E. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph.D Dissertation. Zurich, Switzerland: Swiss Federal Institute of Technology, 1999 [3] Deb K, Agrawal S, Pratap A, et al. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II // Proc of the Parallel Problem Solving from Nature VI Conference. Paris, France, 2000: 849-858 [4] Corne D W, Knowles J D, Oates M J. The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimisation // Proc of the Parallel Problem Solving from Nature VI Conference. Paris, France, 2000: 839-848 [5] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. Technical Report, 103, Zurich, Switzerland: Swiss Federal Institute of Technology. Computer Engineering and Networks Laboratory, 2001 [6] Laumanns M, Thiele L, Zitzler E. Archiving with Guaranteed Convergence and Diversity in Multi-Objective Optimization // Proc of the Genetic and Evolutionary Computation Conference. New York, USA: Morgan Kaufmann Publishers, 2002: 439-447 [7] Sim K B, Kim J Y, Lee D W. Game Theory Based Coevolutionary Algorithm: A New Computational Coevolutionary Approach. International Journal of Control, Automation, and Systems, 2004, 2(4): 463-474 [8] Zitzler E, Thiele L. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans on Evolutionary Computation, 1999, 3(4): 257-271 [9] Yao Xin, Liu Yong, Lin Guangming. Evolutionary Programming Made Faster. IEEE Trans on Evolutionary Computation, 1999, 3(2): 82-102 [10]Iwamatsu M. Generalized Evolutionary Programming with Lévy-Type Mutation. Computer Physics Communications, 2002, 147(8): 729-732 [11] Lee C Y, Yao X. Evolutionary Programming Using Mutations Based on the Levy Probability Distribution. IEEE Trans on Evolutionary Computation, 2004, 8(1): 1-13 [12] Ji Mingjun, Tang Huanwen, Guo Juan. A Single-Point Mutation Evolutionary Programming. Information Processing Letters, 2004, 90(2): 293-299 [13] Zitzler E, Marco L, Stefan B. A Tutorial on Evolutionary Multiobjective Optimization // Proc of the Workshop on Multiple Objective Metaheuristics. Berlin, Germany: Springer, 2004: 3-37 [14]Silverman B W. Density Estimation for Statistics and Data Analysis. London, UK: Chapman and Hall, 1986 [15] Deb K. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 1999, 7(3): 205-230 [16] Dong Hongbin, He Jun, Huang Houkuan, et al. A Mixed Mutation Strategies Evolutionary Programming Based Species Conservation for Function Optimization // Proc of the 4th Mexican International Conference on Artificial Intelligence. Berlin, Germany: Springer-Verlag, 2005: 593-602
2006, Vol. 19 
