Two-phase differential evolution framework for solving optimization problems

Karam M. Sallam, Saber M. Elsayed, Ruhul A. Sarker, Daryl L. Essam

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

7 Citations (Scopus)

Abstract

Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design of an efficient selection mechanism to emphasize the best-performing DE variant has not yet been thoroughly explored. Motivated by this fact, in this paper, a new two-phase (exploration and exploitation) multi-operator DE algorithm is proposed. It starts with the exploration phase, dynamically placing emphasis on the best-performing DE based on two landscape indicators and its performance history, and then repeats this process during the exploitation phase. To judge the performance of this algorithm, a variety of real-world optimization problems taken from different disciplines are solved. According to the results obtained, this algorithm shows superior performance to those of state-of-the-art algorithms.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
EditorsYaochu Jin, Stefanos Kollias
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781509042401
ISBN (Print)9781509042418
DOIs
Publication statusPublished - 9 Feb 2017
Externally publishedYes
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

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