Niche-based and angle-based selection strategies for many-objective evolutionary optimization
Niche-based and angle-based selection strategies for many-objective evolutionary optimization
复制标题
用于多目标进化优化的基于生态位和基于角度的选择策略
DOI：
10.1016/j.ins.2021.04.050
复制
发表时间：
2021-04
影响因子：
8.1
通讯作者：
Tingrui Pei
中科院分区：
文献类型：
--
作者：
周金龙;邹娟;杨圣祥;郑金华;Dunwei Gong;Tingrui Pei
众所周知，平衡种群多样性和收敛在进化多目标优化中起着至关重要的作用。然而，现有的大多数多目标进化算法在求解多目标优化问题时遇到了困难。因此，本文提出了基于小生境和基于角度的多目标进化优化选择策略。该算法包括两种策略：基于小生境的密度估计策略和基于角度的选择策略。这两种策略都被用在环境选择中，以迭代的方式从种群中淘汰最差的个体。具体地说，前者估计每个个体的多样性，并找到种群中最拥挤的区域。后者去除了在同一个生态位中具有弱收敛的个体。对几个著名基准问题的实验研究表明，该算法与目前最先进的六种多目标算法相比具有较强的竞争力。此外，该算法还被验证了对处理约束多目标优化问题具有良好的可扩展性。
It is well known that balancing population diversity and convergence plays a crucial role in evolutionary many-objective optimization. However, most existing multiobjective evolutionary algorithms encounter difficulties in solving many-objective optimization problems. Thus, this paper suggests niche-based and angle-based selection strategies for many-objective evolutionary optimization. In the proposed algorithm, two strategies are included: niche-based density estimation strategy and angle-based selection strategy. Both strategies are employed in the environmental selection to eliminate the worst individual from the population in an iterative way. To be specific, the former estimates the diversity of each individual and finds the most crowded area in the population. The latter removes individuals with weak convergence in the same niche. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with six state-of-the-art many-objective algorithms. Moreover, the proposed algorithm has also been verified to be scalable to deal with constrained many-objective optimization problems.