Abstract
A novel Smart Multi-Objective Particle Swarm Optimisation method - SDMOPSO - is presented in the paper. The method uses the decomposition approach proposed in MOEA/D, whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. The paper also introduces a novel smart approach for sharing information between particles, whereby each particle calculates a new position in advance using its neighbourhood information and shares this new information with the swarm. The results of applying SDMOPSO on five standard MOPs show that SDMOPSO is highly competitive comparing with two state-of-the-art algorithms.
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Al Moubayed, N., Petrovski, A., McCall, J. (2010). A Novel Smart Multi-Objective Particle Swarm Optimisation Using Decomposition. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_1
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DOI: https://doi.org/10.1007/978-3-642-15871-1_1
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