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| 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 |
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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.
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Received: 02 November 2005
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2006, Vol. 19 
