Abstract
D 2 MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leader’s archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D 2 MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.
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Al Moubayed, N., Petrovski, A., McCall, J. (2012). D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_7
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DOI: https://doi.org/10.1007/978-3-642-29124-1_7
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