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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorJiang, Shouyongen
dc.date.accessioned2016-04-12T08:59:26Z
dc.date.available2016-04-12T08:59:26Z
dc.date.issued2016-01-13
dc.identifier.citationYang, S. and Jiang, S. (2016) Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons. IEEE Transactions on Cybernetics, 47 (1), pp. 198-211en
dc.identifier.urihttp://hdl.handle.net/2086/11869
dc.description.abstractDynamic multi-objective optimization has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these dynamic multi-objective optimization problems pose a new challenge to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for dynamic multi-objective optimization, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), non-monotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for dynamic multi-objective optimization problems with different characteristics. Six representative multi-objective evolutionary algorithms from the literature are investigated based on the proposed dynamic multi-objective optimization test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for dynamic multi-objective optimization problems.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectDynamic multiobjective optimizationen
dc.subjectevolutionary algorithmen
dc.subjectbenchmarken
dc.subjectperformance metricen
dc.titleEvolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisonsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TCYB.2015.2510698
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.funderNational Natural Science Foundation of Chinaen
dc.projectidEP/K001310/1en
dc.projectid61273031en
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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