Abstract
This paper presents a mu lti-objective d iversity gu ided Particle Swa rm Op timization a pproach named MOPSOAR wh ich increases di versity perfo rmance of multi-objective Parti cle S warm optimization by u sing A ttraction a nd Re pulsion (AR) m echanism. AR m echanism us es a d iversity measure to control the swar m. Being attractive and repulsive wil l help to overcome t he problem of premature c onvergence. AR mechanism together wi th cr owding di stance co mputation an d mutation operator maintains the diversity of non -dominated set in external archive. The approach is verified by several te st f unction exper iments. Results demo nstrate t hat the p roposed approach i s highly competitive in distribution of non-dominated solutions but still keeps convergence towards the Pareto front.
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