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dc.contributor.authorHelbig, Mardé
dc.contributor.authorZille, Heiner
dc.contributor.authorJavadi, Mahrokh
dc.contributor.authorMostaghim, Sanaz
dc.description.abstractIn the area of multi-objective optimization, a special challenge is dynamic optimization problems. These problems change their optimal values or optimal configurations of input variables over time, making it harder for metaheuristic algorithms to track these changes and find the new optima. To test the search ability of such dynamic multi-objective algorithms, a dynamic benchmark called the Dynamic Distance Minimization Problem (dDMP) was proposed in the literature. The dDMP implements multiple changes, not only in location and fitness values of the Pareto-optimal sets, but also in the complexity of the problem. This work aims to test the performance of two well-known dynamic multi-objective algorithms on different instances of the dDMP with varying complexity. This involves changes in the number of objectives and changes of the distance metric at runtime, which has not been done before with this problem in the literature. The results show that both algorithms struggled to recover after the number of objectives was reduced and then increased again. When the distance metric was changed over time both algorithms performed reasonable well. However, there were gaps in the found Pareto fronts when switching between the Euclidean and the Manhattan distance metrics.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofconferencenameGenetic and Evolutionary Computation Conference Companion (GECCO 2019)
dc.relation.ispartofconferencetitleGECCO' 19 Companion: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartoflocationPrague, Czech Republic
dc.subject.fieldofresearchComputation Theory and Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsMathematics, Interdisciplinary Applications
dc.subject.keywordsDynamic optimization
dc.titlePerformance of Dynamic Algorithms on the Dynamic Distance Minimization Problem
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationHelbig, M; Zille, H; Javadi, M; Mostaghim, S, Performance of Dynamic Algorithms on the Dynamic Distance Minimization Problem, GECCO' 19 Companion: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, 2019, pp. 205-206
gro.hasfulltextNo Full Text
gro.griffith.authorHelbig, Mardé

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    Contains papers delivered by Griffith authors at national and international conferences.

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