一种基于角度惩罚距离的高维多目标进化算法

毕晓君; 王朝

doi:10.11999/JEIT170454

一种基于角度惩罚距离的高维多目标进化算法

doi: 10.11999/JEIT170454

毕晓君¹,
王朝¹

基金项目:

国家自然科学基金(61175126)，中央高校基本科研业务费专项资金(HEUCFP201709)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-05-12
- 修回日期: 2017-09-22
- 刊出日期: 2018-02-19

A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance

BI Xiaojun¹,
WANG Chao¹

Funds:

The National Natural Science Foundation of China (61175126), The Fundamental Research Funds for the Central Universities (HEUCFP201709)

摘要

摘要: 为了使多目标进化算法在收敛性和分布性之间保持平衡，该文提出一种基于角度惩罚距离的高维多目标进化算法(Many-Objective Evolutionary Algorithm based on Angle Penalized Distance, MaOEA-APD)。首先，综合考虑收敛性和分布性在进化不同阶段的重要性，构造一种角度惩罚距离，使两者随进化进程动态平衡；其次，开发基于删除劣质个体的环境选择策略，在提高种群分布性的同时提高收敛性；最后，根据环境选择的原理，设计与之相协调且互补的匹配选择过程，提高算法的整体进化效率。将所提算法与目前国内外性能优异的3种高维多目标进化算法进行对比，实验结果表明在WFG标准测试函数集上，该文算法相对于其他算法，综合性能有了较大的提升。
- 高维多目标优化 /
- 进化算法 /
- 删除策略 /
- 角度惩罚距离
Abstract: In order to balance between convergence and distribution in Multi-Objective Evolutionary Algorithms (MOEAs), a Many-Objective Evolutionary Algorithm based on Angle Penalized Distance (MaOEA-APD) is proposed. Firstly, considering the importance of convergence and diversity in the different stages of the evolutionary process, an angle penalized distance is constructed to dynamically balance between them. Then, the environmental selection based on removing the worse individual is designed to maintain the distribution and improve the convergence. Finally, the mating selection is designed based on the principle of the environmental selection. Both are complement and coordinated to each other for improving the evolutionary efficiency of the algorithm. Compared with three state-of-the-art many-objective evolutionary algorithms (MaOEAs), the experimental results on WFG test suite show that MaOEA-APD has more advantage than other algorithms in terms of the overall performance.
- Many-objective optimization /
- Evolutionary algorithm /
- Elimination strategy /
- Angle penalized distance

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参考文献(23)

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