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This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front compared to two other multiobjective optimization evolutionary algorithms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 209–226, 2006. [ABSTRACT FROM AUTHOR]