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- W890304461 abstract "The diversity of solutions is of great importance for multi-objective evolutionary algorithms. In this paper, a new multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D) is proposed. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors. Then MPSO/D makes each sub-region have a solution to maintain the diversity. Secondly, considering the convergence of solutions, MPSO/D uses the crowding distance to calculate the fitness values of the reserved solutions for selection operator, and uses the neighboring particles of a particle to determine the global best historical position (gbest) of the particle. The proposed algorithm has been compared with NSGAII, MOEA/D and NNIA on sixteen test sets. The experimental results illustrate that the proposed algorithm outperforms NSGAII, MOEA/D and NNIA in terms of convergence and diversity." @default.
- W890304461 created "2016-06-24" @default.
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- W890304461 date "2015-12-01" @default.
- W890304461 modified "2023-10-17" @default.
- W890304461 title "A new multi-objective particle swarm optimization algorithm based on decomposition" @default.
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- W890304461 doi "https://doi.org/10.1016/j.ins.2015.07.018" @default.
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