An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

doi:10.1016/j.ejor.2011.09.025

Return

An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

2011-09-01

245

PRE

Musrrat Ali

Patrick Siarry *

Millie. Pant

DOI：10.1016/j.ejor.2011.09.025

Original

Original request for help

Save

Abstract

En 中文

In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEA5). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.

Keywords:

Evolutionary computation

Global optimization

Multiple objective programming

Opposition-Based Learning

Random localization

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.