On convergence of multi-objective Particle Swarm Optimizers

Several variants of the Particle Swarm Optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization 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 control the convergence behavior of the algorithm to the Pareto front in the objective function space. Limited simulation supports have also been provided to substantiate the theoretical derivations.

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Prithwish Chakraborty, Swagatam Das, Ajith Abraham, Václav Snásel, Gourab Ghosh Roy
• Keywords: Artificial Intelligence, CEC 2010, Multi-objective Optimization Problems, Pareto Optimality, Particle Swarm Optimization