期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 6, 页码 1446-1459出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2548239
关键词
Many-objective optimization particle swarm optimization (MaOPSO); many-objective optimization problem (MaOP); parallel cell coordinate system (PCCS); particle swarm optimization (PSO)
类别
资金
- Fundamental Research Funds for the Central Universities [ZYGX2013J078]
- Sichuan Province Science and Technology Support Project [2015FZ0043]
- China Scholarship Council
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 manyobjective 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.
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