Adaptive mufti-objective particle swarm optimization algorithm

In this article we describe a novel Particle Swarm Optimization (PSO) approach to Multi-objective Optimization (MOO) called Adaptive Multi-objective Particle Swarm Optimization (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 swarming 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 evolutionary algorithms for nine function optimization problems, using different performance measures.