Helbig, MEngelbrecht, AP2013-11-192013-11-192013-06Helbig, 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 2013http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06595776http://hdl.handle.net/10204/7081IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Singapore, 16-19 April 2013. Abstract only attached.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.enDynamic multi-objective optimisation problemsDMOOPComputational intelligencePareto-optimal frontPOFConference PresentationHelbig, M., & Engelbrecht, A. (2013). Benchmarks for dynamic multi-objective optimisation. IEEE Xplore. http://hdl.handle.net/10204/7081Helbig, M, and AP Engelbrecht. "Benchmarks for dynamic multi-objective optimisation." (2013): http://hdl.handle.net/10204/7081Helbig M, Engelbrecht A, Benchmarks for dynamic multi-objective optimisation; IEEE Xplore; 2013. http://hdl.handle.net/10204/7081 .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 -