Improved United Multi-Operator Algorithm 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

9 Citations (Scopus)

Abstract

Although many evolutionary algorithms (EAs) have successfully solved different optimization problems, no single EA has consistently been the best for all these problems. During the last decade, to alleviate this limitation, many proposals which utilize multiple EAs in a single algorithmic framework, called multi-methods or multi-operators, have been introduced. However, there is still room to enhance their performance. In this paper, an improved variant of a united multi-operator algorithm is introduced with few improvements that are capable of providing a balance between diversification and intensification properties during the optimization. The proposed algorithm is tested on the CEC2017 unconstrained benchmark problems, with the results revealing that the proposed algorithm is capable of producing high quality solutions compared with those of state-of-the-art algorithms.

Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
EditorsFernando Von Zuben, Gary Yen, Hisao Ishibuchi
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781509060177
ISBN (Print)9781509060184
DOIs
Publication statusPublished - 28 Sep 2018
Externally publishedYes
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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