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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 6/2020

19.11.2019

Differential evolution algorithm with elite archive and mutation strategies collaboration

verfasst von: Yuzhen Li, Shihao Wang

Erschienen in: Artificial Intelligence Review | Ausgabe 6/2020

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Abstract

This paper proposes a differential evolution algorithm with elite archive and mutation strategies collaboration (EASCDE), wherein two main improvements are presented. Firstly, an elite archive mechanism is introduced to make DE/rand/3 and DE/current-to-best/2 mutation strategies converge faster. Secondly, a mutation strategies collaboration mechanism is developed to tightly combine both strategies to balance global exploration and local exploitation. As a result, EASCDE can effectively keep population diversity in the early stage and significantly enhance convergence speed as well as solution quality in the later stage. The performance of EASCDE is verified by experimental analyses on the well-known test functions. The results demonstrate that EASCDE is superior to other compared competitors in terms of solution precision, convergence speed and stability. Moreover, EASCDE is also an efficient method in dealing with arrival flights scheduling problem.
Vorheriger Artikel An interactive multi-agent reasoning model for sentiment analysis: a case for computational semiotics
Nächster Artikel Systematic review of spell-checkers for highly inflectional languages

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Metadaten
Titel
Differential evolution algorithm with elite archive and mutation strategies collaboration
verfasst von
Yuzhen Li
Shihao Wang
Publikationsdatum
19.11.2019
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 6/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-019-09786-5

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