Evolving Hard and Easy Traveling Salesman Problem Instances: A Multi-objective Approach

被引:0
|
作者
Jiang, He [1 ]
Sun, Wencheng [1 ]
Ren, Zhilei [1 ]
Lai, Xiaochen [1 ]
Piao, Yong [1 ]
机构
[1] Dalian Univ Technol, Sch Software, Dalian, Peoples R China
关键词
TSP; 2-opt; multi-objective optimization algorithm; random forest;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It becomes a great challenge in the research area of metaheuristics to predict the hardness of combinatorial optimization problem instances for a given algorithm. In this study, we focus on the hardness of the traveling salesman problem (TSP) for 2-opt. In the existing literature, two approaches are available to measure the hardness of TSP instances for 2-opt based on the single objective: the efficiency or the effectiveness of 2-opt. However, these two objectives may conflict with each other. To address this issue, we combine both objectives to evaluate the hardness of TSP instances, and evolve instances by a multi-objective optimization algorithm. Experiments demonstrate that the multi-objective approach discovers new relationships between features and hardness of the instances. Meanwhile, this new approach facilitates us to predict the distribution of instances in the objective space.
引用
收藏
页码:216 / 227
页数:12
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