An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

IEEE Transactions on Cybernetics
Shouyong Jiang, Shengxiang Yang

Abstract

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other appr...Continue Reading

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Citations

Jan 15, 2016·IEEE Transactions on Cybernetics·Shouyong Jiang, Shengxiang Yang
Jun 29, 2016·IEEE Transactions on Cybernetics·Minh Luan NguyenAlvis C M Fong
Jun 22, 2017·International Journal of Neural Systems·Alberto FernándezFrancisco Herrera
Jul 23, 2016·IEEE Transactions on Cybernetics·Xiao ZhangMinming Li
Aug 9, 2020·Computational Intelligence and Neuroscience·Ya-Nan FengZhi-Yuan Chen

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