Improving Neighborhood Exploration Mechanism to Speed up PLS

被引：1
|
作者：
Kang, Yuhao [1 ]
Shi, Jialong [1 ,2 ]
Sun, Jianyong [1 ]
Fan, Ye [3 ]
机构：
[1] Xi An Jiao Tong Univ, Xian, Shaanxi, Peoples R China
[2] Sichuan Digital Econ Ind Dev Res Inst, Chengdu, Sichuan, Peoples R China
[3] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China
来源：
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023 | 2023年
基金：
中国国家自然科学基金;
关键词：
Local Search; Metaheuristics; Speedup technique; Combinatorial optimization; Multi-objective optimization; ALGORITHM;
D O I：
10.1145/3583131.3590445
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As an extension of local search for multiobjective case, the basic version of Pareto Local Search (PLS) suffers from a poor anytime behavior. Researches have been carried out to overcome this drawback from different aspects. In this paper, we focus on the mechanism of neighborhood exploration in bi-objective Travelling Salesman Problems (bTSPs). Inspired by existing fast local search strategies for single objective TSP, we propose two speed-up strategies to help PLS quickly find promising neighboring solutions in bTSPs. In the experimental studies, we investigate the sensitivity of parameters and test the performance of several PLS variants with different combinations of the two strategies. The experimental results verify the effectiveness of the two strategies and their combination.
引用
收藏
页码：688 / 694
页数：7
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