2024年7月25日，Elsevier 旗下top期刊《Swarm and Evolutionary Computation》在线发表了云南师范大学信息学院甘健侯教授团队的最新研究成果《RBSS: A fast subset selection strategy for multi-objective optimization》。云南师范大学信息学院为第一单位，通讯作者为云南师范大学信息学院甘健侯教授。

https://www.sciencedirect.com/science/article/abs/pii/S2210650224001974

Abstract

Multi-objective optimization problems (MOPs) aim to obtain a set of Pareto-optimal solutions, and as the number of objectives increases, the quantity of these optimal solutions grows exponentially. However, a plethora of optimal solutions can impose significant decision stress on decision-makers. Subset selection, as the extension of a model, can extract a representative set of solutions, thereby alleviating the decision-makers’ choice pressure. In addition, extending a model undoubtedly incurs additional time costs. To cope with the foregoing issues, a fast subset selection method named ranking-based subset selection (RBSS) is proposed in this paper. It can efficiently select a small number of optimal solutions within an unbounded external archive and can be directly applied to any multi-objective evolutionary algorithm. This allows it to maintain good distribution and diversity with very little time investment. We employed a ranking-based approach to map the objective space to a ranking space (an integer space) defined by us and then selected the corresponding subset in the ranking space. The well-behaved mathematical properties of the ranking space and the advantages of using integer calculations accelerated the subset selection process. Experimental results indicate that compared to several state-of-the-art subset selection methods, RBSS is capable of selecting a set of representative and diverse solutions across different types of MOPs, while consuming significantly less time. Specifically, for problems where the Pareto front is a two-dimensional manifold and a one-dimensional manifold, the time consumption of RBSS is approximately only 0.028% to 27.5% and 4.6e−4% to 0.15% of that required by other algorithms, respectively.

参考阅读：

云师大民族教育信息化教育部重点实验室甘健侯、周菊香在《Knowledge-Based Systems》》发表研究成果

云师大信息学院甘健侯教授课题组在国际知名TOP期刊《人工智能的工程应用》上发表研究成果

云南师范大学甘健侯教授

甘健侯-云南师范大学-教育学部