Differential evolution enhanced with multiple dimensional scaling

Minjuan Liu, Wei Huang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Differential evolution algorithm is a well-known intelligent optimization algorithm. In the search process, the algorithm tends to converge prematurely, making the population trapped in local optima. To solve this problem, this paper proposes a new differential evolution algorithm based on multiple dimensional scaling (MDS). We tested the performance of the algorithm on 11 benchmark functions. Experimental results show that the proposed algorithm can achieve higher accuracy when compared with some other evolutionary algorithms reported in the literatures.

Original languageEnglish
Title of host publicationICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication
EditorsWenzheng Li, Guomin Zuo
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages461-464
Number of pages4
ISBN (Electronic)9781728111896
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019 - Beijing, China
Duration: 12 Jul 201914 Jul 2019

Publication series

NameICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication

Conference

Conference9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019
Country/TerritoryChina
CityBeijing
Period12/07/1914/07/19

Keywords

  • differential evolution
  • local optima
  • multiple dimensional scaling

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