DOI: 10.4018/IJAMC.2018100104 International Journal of Applied Metaheuristic Computing Volume 9 • Issue 4 • October-December 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 71 An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search Swapnil Prakash Kapse, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India Shankar Krishnapillai, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India ABSTRACT This article demonstrates the implementation of a novel local search approach based on Utopia point guided search, thus improving the exploration ability of multi- objective Particle Swarm Optimization. This strategy searches for best particles based on the criteria of seeking solutions closer to the Utopia point, thus improving the convergence to the Pareto-optimal front. The elite non-dominated selected particles are stored in an archive and updated at every iteration based on least crowding distance criteria. The leader is chosen among the candidates in the archive using the same guided search. From the simulation results based on many benchmark tests, the new algorithm gives better convergence and diversity when compared to existing several algorithms such as NSGA-II, CMOPSO, SMPSO, PSNS, DE+MOPSO and AMALGAM. Finally, the proposed algorithm is used to solve mechanical design based multi-objective optimization problems from the literature, where it shows the same advantages. KeywORdS IGD, Local Search, Multi-Objective Particle Swarm Optimization, Obtained Pareto-Front, Pareto-Optimal Front, PSO, Scaling Factor (r), Utopia Point INTROdUCTION Multi-objective optimization algorithms aim at improving the convergence to the True Pareto- optimal front, and also maintain good diversity of population (Sierra & Coello, 2006). Closeness of the obtained Pareto-optimal front to the Pareto optimal Front is referred to as convergence and the uniform distribution of solutions is referred to as diversity (Baviskar & Krishnapillai, 2016). Various evolutionary algorithms are proposed in the field of multi-objective optimization such as NSGA, SPEA, NSGA-II, AMALGAM, SMPSO and CMOPSO which will be discussed in the succeeding sections. Zitzler and Thiele (1991) presented Strength Pareto Evolutionary Algorithm (SPEA), which used an archive population set to store previously generated non- dominated solutions and update it as and when new non- dominated solutions and found. PSO belongs to swarm intelligence category of techniques. PSO is a population based heuristic optimization method which is motivated by the behaviour of birds in a flock foraging for food and introduced by Kennedy (1995). Introduction of inertia weight in PSO improved the convergence of PSO (Shi & Eberhart 1998). The original PSO was introduced for single objective optimization and hence it was modified to Multi-Objective Particle Swarm Optimization (MOPSO) using a ring topology by Moore and Chapman (1999). Coello et al. (2002) proposed a multi- objective approach to PSO wherein the concept of Pareo dominance was used to determine the flight direction. Later, PSO was further modified and improved due to its