.png){kind=icon}
返回
IT-CEMOP: An iterative co-evolutionary algorithm for multiobjective optimization problem with nonlinear constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2006.05.095.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most of them were based on penalty functions for handling nonlinear constraints by genetic algorithms. However the performance of these methods is highly problem-dependent, many methods require additional tuning of several parameters. In this paper, we present a new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The algorithm maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of epsilon-dominance. The use of epsilon-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation by choosing an appropriate e value, which guarantees convergence and diversity. The results, provided by the proposed algorithm for six benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems. (c) 2006 Elsevier Inc. All rights reserved.
Keyword:
multiobjective nonlinear programming
multiobjective evolutionary algorithms
genetic algorithms
epsilon-dominance
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
暂无机构信息
引用论文
.png){kind=icon}
An effective genetic algorithm approach multiobjective resource allocation problems (MORAPs)一种有效的遗传算法解决多目标资源分配问题 (MORAPs)
PREAI
分享收藏分享收藏
查看更多内容