Abstract
In this paper, we propose a hybrid multiobjective evolutionary algorithm combining two heuristic optimization techniques. Our approach integrates the merits of both genetic algorithm (GA) and particle swarm optimization (PSO), and has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which is flown through the search space. In order to get approximate nondominated solutions PND, an evolution of this particle is performed. Secondly, the local search (LS) scheme is implemented as a neighborhood search engine to improve the solution quality, where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Finally, various kinds of multiobjective (MO) benchmark problems including the set of benchmark functions provided for CEC09 have been reported to stress the importance of hybridization algorithms in generating Pareto optimal sets for multiobjective optimization problems. (C) 2011 Elsevier B.V. All rights reserved.