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Computational Optimization and Applications

, Volume 49, Issue 2, pp 379–400 | Cite as

MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm

  • Yann Cooren
  • Maurice Clerc
  • Patrick Siarry
Article

Abstract

This paper presents MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization (PSO) algorithm. Metaheuristics have the drawback of being very dependent on their parameter values. Then, performances are strongly related to the fitting of parameters. Usually, such tuning is a lengthy, time consuming and delicate process. The aim of this paper is to present and to evaluate MO-TRIBES, which is an adaptive algorithm, designed for multiobjective optimization, allowing to avoid the parameter fitting step. A global description of TRIBES and a comparison with other algorithms are provided. Using an adaptive algorithm means that adaptation rules must be defined. Swarm’s structure and strategies of displacement of the particles are modified during the process according to the tribes behaviors. The choice of the final solutions is made using the Pareto dominance criterion. Rules based on crowding distance have been incorporated in order to maintain diversity along the Pareto Front. Preliminary simulations are provided and compared with the best known algorithms. These results show that MO-TRIBES is a promising alternative to tackle multiobjective problems without the constraint of parameter fitting.

Keywords

Particle swarm optimization Parameter-free Pareto dominance Crowding distance Adaptive 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Laboratoire Images, Signaux et Systèmes Intelligents, LiSSi, E.A. 3956Université de Paris 12CréteilFrance

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