Helbig, MEngelbrecht, AP2012-11-192012-11-192012Helbig, M and Engelbrecht, AP. Dynamic multi-objective optimization using PSO. In: Alba, E, Nakib, A, and Siarry, P (eds), Metaheuristics for Dynamic Optimization, (Studies in Computational Intelligence, Volume 433), pp. 147-188, DOI 10.1007/978-3-642-30665-5_8. Springer, Berlin, Germany.978-3-642-30664-8978-3-642-30665-5https://www.springer.com/pay+per+view?SGWID=0-1740713-3131-0-0http://hdl.handle.net/10204/6330Copyright: 2013 SpringerVerlag. This is the post-print version of the work. The definitive version is published in Alba, E, Nakib, A, and Siarry, P (eds), Metaheuristics for Dynamic Optimization, (Studies in Computational Intelligence, Volume 433), pp. 147-188, DOI 10.1007/978-3-642-30665-5_8Dynamic multi-objective optimisation problems (DMOOPs) occur in many situations in the real world. These optimisation problems do not have a single goal to solve, but many goals that are in conflict with one another - improvement in one goal leads to deterioration of another. Therefore, when solving DMOOPs, an algorithm attempts to find the set of optimal solutions, referred to as the Paretooptimal front (POF). Each DMOOP also has a number of boundary constraints that limits the search space. When the particles of a particle swarm optimisation (PSO) algorithm moves outside the search space, an approach should be followed to manage violation of the boundary constraints. This chapter investigates the effect of various approaches to manage boundary constraint violations on the performance of the Dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO) algorithm when solving DMOOPs. Furthermore, the performance of DVEPSO is compared against the performance of three other state-of-the-art dynamic multi-objective optimisation (DMOO) algorithms.enDynamic multi-objective optimisationParticle swarm optimisationBook ChapterHelbig, M., & Engelbrecht, A. (2012). Dynamic multi-Objective optimization using PSO., <i>Workflow;9690</i> Springer. http://hdl.handle.net/10204/6330Helbig, M, and AP Engelbrecht. "Dynamic multi-objective optimization using PSO" In <i>WORKFLOW;9690</i>, n.p.: Springer. 2012. http://hdl.handle.net/10204/6330.Helbig M, Engelbrecht A. Dynamic multi-objective optimization using PSO.. Workflow;9690. [place unknown]: Springer; 2012. [cited yyyy month dd]. http://hdl.handle.net/10204/6330.TY - Book Chapter AU - Helbig, M AU - Engelbrecht, AP AB - Dynamic multi-objective optimisation problems (DMOOPs) occur in many situations in the real world. These optimisation problems do not have a single goal to solve, but many goals that are in conflict with one another - improvement in one goal leads to deterioration of another. Therefore, when solving DMOOPs, an algorithm attempts to find the set of optimal solutions, referred to as the Paretooptimal front (POF). Each DMOOP also has a number of boundary constraints that limits the search space. When the particles of a particle swarm optimisation (PSO) algorithm moves outside the search space, an approach should be followed to manage violation of the boundary constraints. This chapter investigates the effect of various approaches to manage boundary constraint violations on the performance of the Dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO) algorithm when solving DMOOPs. Furthermore, the performance of DVEPSO is compared against the performance of three other state-of-the-art dynamic multi-objective optimisation (DMOO) algorithms. DA - 2012 DB - ResearchSpace DP - CSIR KW - Dynamic multi-objective optimisation KW - Particle swarm optimisation LK - https://researchspace.csir.co.za PY - 2012 SM - 978-3-642-30664-8 SM - 978-3-642-30665-5 T1 - Dynamic multi-objective optimization using PSO TI - Dynamic multi-objective optimization using PSO UR - http://hdl.handle.net/10204/6330 ER -