Constrained Multi-Objective Optimization Algorithm with Diversity Enhanced Differential Evolution

被引：0
|
作者：
Qu, Bo-Yang [1 ]
Suganthan, Ponnuthurai Nagaratnam [1 ]
机构：
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源：
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2010年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Constrained multi-objective differential evolution (CMODE) is a population-based stochastic search technique for solving constrained multi-objective optimization problems. Although CMODE is a powerful and efficient search algorithm, it frequently suffers from pre-mature convergence, especially when there are numerous local Pareto optimal solutions. In this paper, a diversity enhanced constrained multi-objective differential evolution (DE-CMODE) is proposed to overcome the pre-mature convergence problem. The performance of DE-MODE is evaluated on a set of 8 benchmark problems. As shown in the experimental results, the DE-CMODE performs either better or similar to the classical CMODE.
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页数：5
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