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D2MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance

  • Noura Al Moubayed
  • Andrei Petrovski
  • John McCall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7245)

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.

Keywords

Pareto Front Pareto Optimal Solution Objective Space Aggregation Function Swarm Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Noura Al Moubayed
    • 1
  • Andrei Petrovski
    • 1
  • John McCall
    • 1
  1. 1.School of ComputingRobert Gordon UniversityAberdeenUK

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