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)


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.


Pareto Front Pareto Optimal Solution Objective Space Aggregation Function Swarm Size 
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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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