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- W2337108669 abstract "It is a daunting challenge to balance the convergence and diversity of an approximate Pareto front in a many-objective optimization evolutionary algorithm. A novel algorithm, named many-objective particle swarm optimization with the two-stage strategy and parallel cell coordinate system (PCCS), is proposed in this paper to improve the comprehensive performance in terms of the convergence and diversity. In the proposed two-stage strategy, the convergence and diversity are separately emphasized at different stages by a single-objective optimizer and a many-objective optimizer, respectively. A PCCS is exploited to manage the diversity, such as maintaining a diverse archive, identifying the dominance resistant solutions, and selecting the diversified solutions. In addition, a leader group is used for selecting the global best solutions to balance the exploitation and exploration of a population. The experimental results illustrate that the proposed algorithm outperforms six chosen state-of-the-art designs in terms of the inverted generational distance and hypervolume over the DTLZ test suite." @default.
- W2337108669 created "2016-06-24" @default.
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- W2337108669 date "2017-06-01" @default.
- W2337108669 modified "2023-10-03" @default.
- W2337108669 title "Many-Objective Particle Swarm Optimization Using Two-Stage Strategy and Parallel Cell Coordinate System" @default.
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- W2337108669 doi "https://doi.org/10.1109/tcyb.2016.2548239" @default.
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