Decomposition-Based Multi-Objective Evolutionary algorithms (DBmoEA), such as Multiple Single Objective Pareto Sampling (MSOPS) and Multiobjective Evolutionary algorithm based on Decomposition (moEA/D), have been succ...
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ISBN:
(纸本)9781479938414
Decomposition-Based Multi-Objective Evolutionary algorithms (DBmoEA), such as Multiple Single Objective Pareto Sampling (MSOPS) and Multiobjective Evolutionary algorithm based on Decomposition (moEA/D), have been successfully applied in finding Pareto-optimal fronts in Multiobjective Optimization Problems (moPs), two or three-objective in general. DBmoEA decomposes one moP into multiple Single-objective Optimization Problems (SOPs) where the convergence of approximated front is facilitated by finding the optimal solution of each SOP and its diversity is preserved by a group of well distributed SOPs. However, when solving problems with many objectives, one single solution can be the optimal solution of multiple SOPs which inadvertently leads to a severe loss of population diversity. In this paper, we propose a new diversity improvement method incorporated into a modified DBmoEA to directly handle this challenge. The design includes two steps. First, a few number of weight vectors guide the whole population towards a small number of solutions nearby the true Pareto front. Afterwards, initialize a subpopulation around each solution and diversify them toward well distribution. As a case study, a new algorithm based on this design is compared with three state-of-the-art DBmoEAs, moEA/D, MSOPS, and mo-nsga-ii. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the chosen competitors for solving many-objective optimization problems.
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