A pareto-based differential evolution algorithm for multi-objective optimization problems

2010 Chinese Control and Decision Conference Pub Date : 2010-05-26 DOI:10.1109/CCDC.2010.5498305

Ruhai Lei, Yuhu Cheng

引用次数: 7

Abstract

A new Pareto-based differential evolution (PDE) algorithm for solving multi-objective optimization problems was proposed by applying the nondominated sorting and ranking selection procedure developed in NSGA-II to select nondominated individuals to constitute a nondominated solution set. The PDE algorithm was validated using eight benchmark cases. The experimental results show that PDE, compared with NSGA-II algorithm, can find many Pareto optimal solutions distributed onto the Pareto front uniformly, which is an effective method to solve multi-objective optimization problems.

查看原文本刊更多论文

多目标优化问题的pareto差分进化算法

利用NSGA-II中开发的非支配排序和排序选择过程，选择非支配个体构成非支配解集，提出了一种新的求解多目标优化问题的pareto差分进化(PDE)算法。使用8个基准案例对PDE算法进行了验证。实验结果表明，与NSGA-II算法相比，PDE算法能够找到均匀分布在Pareto前沿的多个Pareto最优解，是求解多目标优化问题的有效方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2010 Chinese Control and Decision Conference

自引率

0.00%

发文量