Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems

Karam M. Sallam, Saber M. Elsayed, Ripon K. Chakrabortty, Michael J. Ryan

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

12 Citations (Scopus)

Abstract

In recent years, several multi-method and multi-operator-based algorithms have been proposed for solving optimization problems. Generally, their performance is better than other algorithms that based on a single operator and/or algorithm. However, they do not perform consistently well over all the problems tested in the literature. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple differential evolution operators, with more emphasis placed on the best-performing operator. The performance of the proposed algorithm is tested by solving 10 problems with 5, 10, 15 and 20 dimensions taken from CEC2020 competition on single objective bound constrained optimization, with its results outperforming both single operator-based and different state-of-the-art algorithms.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
EditorsYaochu Jin
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781728169293
ISBN (Print)9781728169309
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

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