Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution

  • Upali Wickramasinghe
  • Xiaodong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm towards the Pareto optimal front. The proposed algorithm uses a Differential Evolution operator to create the leaders. These leaders can successfully guide the other particles towards the Pareto optimal front for various types of test problems. This simple yet robust algorithm is effective compared with existing multi-objective particle swarm algorithms.


Hybrid Particle Swarm Differential Evolution Multi-objective optimization Multi-modal problems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Upali Wickramasinghe
    • 1
  • Xiaodong Li
    • 1
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia

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