On convergence of the multi-objective particle swarm optimizers

  • Authors:

  • Prithwish Chakraborty;Swagatam Das;Gourab Ghosh Roy;Ajith Abraham

  • Affiliations:

  • Dept. of Electronics and Telecommunication Eng., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Eng., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Eng., Jadavpur University, Kolkata, India;Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, Auburn, Washington 98071-2259, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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Abstract

Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations.