Gaining-Sharing Knowledge Based Algorithm with Adaptive Parameters Hybrid with IMODE Algorithm for Solving CEC 2021 Benchmark Problems

Ali Wagdy Mohamed, Anas A Hadi, Prachi Agrawal, Karam M Sallam, Ali Khater Mohamed

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

5 Citations (Scopus)

Abstract

The initiative to introduce new benchmark problems has drawn attention to the development of new optimization algorithms. Recently, a set of constrained benchmark problems has been developed as a addition to CEC benchmark series. This paper proposed a hybrid variant of gaining sharing knowledge based algorithm with adaptive parameters and improved multi-operator differential evolution (IMODE) algorithm, called APGSK-IMODE. It enhanced the performance of recently developed adaptive gaining sharing knowledge based algorithm. The performance of APGSK-IMODE has been tested on CEC2021 benchmark problems which contains 10 test functions with dimensions 10 and 20. The results obtained from the proposed algorithm have been compared with those obtained from the rival algorithms. The results elaborate the superiority of APGSK-IMODE. APGSK-IMODE outperforms the competing algorithms with regard to quality of solution, robustness and convergence.
Original languageEnglish
Title of host publication2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
EditorsJacek Mańdziuk, Hussein Abbass
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages841-848
Number of pages8
ISBN (Electronic)9781728183923
ISBN (Print)9781728183930, 9781728183947
DOIs
Publication statusPublished - 2021
Externally publishedYes

Publication series

Name2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings

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