Adaptive Multi-objective Particle Swarm Optimization Algorithm

Abstract

In this article we describe a novel Particle Swarm Optimization (PSO) approach to Multi-objective Optimization (MOO) called Adaptive Multi-objective Particle Swarm Op- timization (AMOPSO). AMOPSO algorithm's novelty lies in its adaptive nature, that is attained by incorporating inertia and the acceleration coefficient as control variables with usual optimization variables, and evolving these through the swarm- ing procedure. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated front. AMOPSO has been compared with some recently developed multi-objective PSO techniques and evo- lutionary algorithms for nine function optimization problems, using different performance measures.