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dc.contributor.authorCheng, Jixiangen
dc.contributor.authorZhang, Gexiangen
dc.contributor.authorCaraffini, Fabioen
dc.contributor.authorNeri, Ferranteen
dc.date.accessioned2016-03-30T15:02:41Z
dc.date.available2016-03-30T15:02:41Z
dc.date.issued2015-02-01
dc.identifier.citationCheng, J., Zhang, G., Caraffini, F. and Neri, F. (2015) Multicriteria adaptive differential evolution for global numerical optimization. Integrated Computer-Aided Engineering, 22 (2), pp. 103-107en
dc.identifier.urihttp://hdl.handle.net/2086/11729
dc.description.abstractDifferential evolution (DE) has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated parameter values is time-consuming. Moreover, to achieve good performance, DE often requires different strategies combined with different parameter values at different evolution stages. Thus integrating several strategies in one algorithm and determining the application rate of each strategy as well as its associated parameter values online become an ad-hoc research topic. This paper proposes a novel DE algorithm, called multicriteria adaptive DE (MADE), for global numerical optimization. In MADE, a multicriteria adaptation scheme is introduced to determine the trial vector generation strategies and the control parameters of each strategy are separately adjusted according to their most recently successful values. In the multicriteria adaptation scheme, the impacts of an operator application are measured in terms of exploitation and exploration capabilities and correspondingly a multi-objective decision procedure is introduced to aggregate the impacts. Thirty-eight scale numerical optimization problems with various characteristics and two real-world problems are applied to test the proposed idea. Results show that MADE is superior or competitive to six well-known DE variants in terms of solution quality and convergence performance.en
dc.language.isoen_USen
dc.publisherIOS Pressen
dc.subjectDifferential evolutionen
dc.subjectAdaptive algorithmsen
dc.subjectEvolutionary computationen
dc.subjectmulticriteria adaptive systemsen
dc.subjectMeta-heuristicsen
dc.subjectOptimisationen
dc.titleMulticriteria adaptive differential evolution for global numerical optimizationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.3233/ICA-150481
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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