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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.

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In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.