期刊
APPLIED INTELLIGENCE
卷 48, 期 1, 页码 118-141出版社
SPRINGER
DOI: 10.1007/s10489-017-0958-4
关键词
Many-objective optimization; NSGA-III; Convergence; Adaptive penalty distance; Niche-elimination operation
资金
- National Natural Science Foundation of China [61175126]
- International S&T Cooperation Program of China [2015DFG12150]
Decomposition-based multi-objective evolutionary algorithms have been found to be very promising for many-objective optimization. The recently presented non-dominated sorting genetic algorithm III (NSGA-III) employs the decomposition idea to efficiently promote the population diversity. However, due to the low selection pressure of the Pareto-dominance relation the convergence of NSGA-III could still be improved. For this purpose, an improved NSGA-III algorithm based on niche-elimination operation (we call it NSGA-III-NE) is proposed. In the proposed algorithm, an adaptive penalty distance (APD) function is presented to consider the importance of convergence and diversity in the different stages of the evolutionary process. Moreover, the niche-elimination operation is designed by exploiting the niching technique and the worse-elimination strategy. The niching technique identifies the most crowded subregion, and the worse-elimination strategy finds and further eliminates the worst individual. The proposed NSGA-III-NE is tested on a number of well-known benchmark problems with up to fifteen objectives and shows the competitive performance compared with five state-of-the-art decomposition-based algorithms. Additionally, a vector angle based selection strategy is also proposed for handling irregular Pareto fronts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据