Journal
INFORMATION SCIENCES
Volume 177, Issue 22, Pages 5033-5049Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2007.06.018
Keywords
multi-objective optimization; pareto dominance; Particle Swarm Optimization
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In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for I I function optimization problems, using different performance measures. (C) 2007 Elsevier Inc. All rights reserved.
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