Multi-Modal Multi-Objective Traveling Salesman Problem and its Evolutionary Optimizer

Liu，Yiping1; Xu，Liting1; Han，Yuyan2; Masuyama，Naoki3; Nojima，Yusuke3; Ishibuchi，Hisao4; Yen，Gary G.1

10.1109/SMC52423.2021.9658818

2021

1062-922X

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

770-777

A multi-modal multi-objective optimization problem (MMOP) may have equivalent Pareto optimal solutions. These solutions are different in the decision space but correspond to the same objective vector. Searching for equivalent Pareto optimal solutions with evolutionary algorithms is a hot topic in recent years. However, most existing researches are about continuous MMOPs, whereas there are few studies on discrete MMOPs. In this paper, we discuss the property of the multi-modal multi-objective traveling salesman problem and present a set of test problems. Then, we propose an evolutionary optimizer to solve the problem. Experimental results show that our evolutionary optimizer can find more equivalent Pareto optimal solutions than traditional multi-objective evolutionary optimizers on the test problems.

其他

英语

EI

20220711616873

Evolutionary algorithms ; Multiobjective optimization ; Optimal systems ; Traveling salesman problem ; Vector spaces

Operations Research:912.3 ; Mathematics:921 ; Optimization Techniques:921.5 ; Systems Science:961

2-s2.0-85124263926

Scopus

1.College of Computer Science and Electronic Engineering,Hunan University,Hunan,Changsha,China
2.School of Computer Science,Liaocheng University,Shandong,Liaocheng,China
3.Osaka Prefecture University,Department of Computer Science and Intelligent Systems,Osaka,Sakai,Japan
4.Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain- Inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,518055,China

Liu，Yiping,Xu，Liting,Han，Yuyan,et al. Multi-Modal Multi-Objective Traveling Salesman Problem and its Evolutionary Optimizer[C],2021:770-777.