Benchmarks for dynamic multi-objective optimisation algorithms

Show simple item record Helbig, Marde Engelbrecht, Andries P. 2015-02-26T06:32:25Z 2015-02-26T06:32:25Z 2014-01
dc.description.abstract Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed. en_ZA
dc.description.librarian hj2015 en_ZA
dc.description.uri en_ZA
dc.identifier.citation Helbig, M & Engelbrecht, AP 2014, 'Benchmarks for dynamic multi-objective optimisation algorithms', ACM Computing Surveys, vol. 46, no. 3, art. 37, pp. 1-33. en_ZA
dc.identifier.issn 0360-0300 (print)
dc.identifier.issn 1557-7341 (online)
dc.identifier.issn 10.1145/2517649
dc.language.iso en en_ZA
dc.publisher Association for Computing en_ZA
dc.rights © 2014 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, vol. 46, no. 3, pp. 1-33, 2014. doi : 10.1145/2517649. ACM Computing Surveys is available online at : en_ZA
dc.subject Benchmark functions en_ZA
dc.subject Complex Pareto-optimal set en_ZA
dc.subject Deceptive Pareto-optimal front en_ZA
dc.subject Dynamic multi-objective optimisation (DMOO) en_ZA
dc.subject Dynamic multi-objective optimisation problems (DMOOPs) en_ZA
dc.subject Ideal benchmark function suite en_ZA
dc.subject Isolated Pareto-optimal front en_ZA
dc.subject Industrial efficiency en_ZA
dc.subject Benchmarking (Management) en_ZA
dc.subject Optimal control theory en_ZA
dc.subject Mathematical optimization en_ZA
dc.subject Algorithms en_ZA
dc.title Benchmarks for dynamic multi-objective optimisation algorithms en_ZA
dc.type Postprint Article en_ZA

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