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Benchmarks for dynamic multi-objective optimisation

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dc.contributor.author Helbig, M
dc.contributor.author Engelbrecht, AP
dc.date.accessioned 2013-11-19T13:43:08Z
dc.date.available 2013-11-19T13:43:08Z
dc.date.issued 2013-06
dc.identifier.citation Helbig, M and Engelbrecht, A. P. 2013. Benchmarks for dynamic multi-objective optimisation. In: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Singapore, 16-19 April 2013 en_US
dc.identifier.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06595776
dc.identifier.uri http://hdl.handle.net/10204/7081
dc.description IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Singapore, 16-19 April 2013. Abstract only attached. en_US
dc.description.abstract When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi-objective optimisation (DMOO) there are no standard benchmark functions that are used. This article proposes characteristics of an ideal set of DMOO benchmark functions, as well as suggested DMOOPs for each characteristic. The limitations of current DMOOPs and studies of dynamic multi-objective optimisation algorithms (DMOAs) are highlighted. In addition, 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) are introduced to address identified limitations of current DMOOPs. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.relation.ispartofseries Workflow;11373
dc.subject Dynamic multi-objective optimisation problems en_US
dc.subject DMOOP en_US
dc.subject Computational intelligence en_US
dc.subject Pareto-optimal front en_US
dc.subject POF en_US
dc.title Benchmarks for dynamic multi-objective optimisation en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Helbig, M., & Engelbrecht, A. (2013). Benchmarks for dynamic multi-objective optimisation. IEEE Xplore. http://hdl.handle.net/10204/7081 en_ZA
dc.identifier.chicagocitation Helbig, M, and AP Engelbrecht. "Benchmarks for dynamic multi-objective optimisation." (2013): http://hdl.handle.net/10204/7081 en_ZA
dc.identifier.vancouvercitation Helbig M, Engelbrecht A, Benchmarks for dynamic multi-objective optimisation; IEEE Xplore; 2013. http://hdl.handle.net/10204/7081 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Helbig, M AU - Engelbrecht, AP AB - When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi-objective optimisation (DMOO) there are no standard benchmark functions that are used. This article proposes characteristics of an ideal set of DMOO benchmark functions, as well as suggested DMOOPs for each characteristic. The limitations of current DMOOPs and studies of dynamic multi-objective optimisation algorithms (DMOAs) are highlighted. In addition, 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) are introduced to address identified limitations of current DMOOPs. DA - 2013-06 DB - ResearchSpace DP - CSIR KW - Dynamic multi-objective optimisation problems KW - DMOOP KW - Computational intelligence KW - Pareto-optimal front KW - POF LK - https://researchspace.csir.co.za PY - 2013 T1 - Benchmarks for dynamic multi-objective optimisation TI - Benchmarks for dynamic multi-objective optimisation UR - http://hdl.handle.net/10204/7081 ER - en_ZA


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