Speciated evolutionary algorithm for dynamic constrained optimisation

Xiaofen LU*, Ke TANG, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

8 Citations (Scopus)

Abstract

Dynamic constrained optimisation problems (DCOPs) have specific characteristics that do not exist in dynamic optimisation problems with bounded constraints or without constraints. This poses difficulties for some existing dynamic optimisation strategies. The maintaining/ introducing diversity approaches might become less effective due to the presence of infeasible areas, and thus might not well handle with the switch of global optima between disconnected feasible regions. In this paper, a speciation-based approach was firstly proposed to overcome this, which utilizes deterministic crowding to maintain diversity, assortative mating and local search to promote exploitation, as well as feasibility rules to deal with constraints. The experimental studies demonstrate that the newly proposed method generally outperforms the state-of-theart algorithms on a benchmark set of DCOPs. © Springer International Publishing AG 2016.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings
EditorsJulia HANDL, Emma HART, Peter R. LEWIS, Manuel LÓPEZ-IBÁÑEZ, Gabriela OCHOA, Ben PAECHTER
PublisherSpringer
Pages203-213
Number of pages11
ISBN (Electronic)9783319458236
ISBN (Print)9783319458229
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event14th International Conference on Parallel Problem Solving from Nature - Edinburgh, Scotland, United Kingdom
Duration: 17 Sept 201621 Sept 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9921
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Parallel Problem Solving from Nature
Abbreviated titlePPSN 2016
Country/TerritoryUnited Kingdom
CityScotland
Period17/09/1621/09/16

Bibliographical note

This work was partially supported by NSFC (Grant No. 61329302), EPSRC (Grant No. EP/K001523/1), and Royal Society Newton Advanced Fellowship (Ref. no. NA150123). The authors thank Stefan Menzel for giving the valuable advice.

Keywords

  • Deterministic crowding
  • Dynamic constrained optimisation problem
  • Evolutionary algorithm
  • Local search
  • Speciation

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