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A Novel Smart Multi-Objective Particle Swarm Optimisation Using Decomposition

  • Noura Al Moubayed
  • Andrei Petrovski
  • John McCall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)

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.

Keywords

Particle Swarm Optimisation Particle Swarm Pareto Front Multiobjective Optimization Pareto Optimal Solution 
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 2010

Authors and Affiliations

  • Noura Al Moubayed
    • 1
  • Andrei Petrovski
    • 1
  • John McCall
    • 1
  1. 1.Robert Gordon University, AberdeenAberdeenUK

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