Improving Neighborhood Exploration Mechanism to Speed up PLS

Yuhao Kang, Jialong Shi, Jianyong Sun, Ye Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationGECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages688-694
Number of pages7
ISBN (Electronic)9798400701191
DOIs
StatePublished - 15 Jul 2023
Event2023 Genetic and Evolutionary Computation Conference, GECCO 2023 - Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023

Publication series

NameGECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference

Conference

Conference2023 Genetic and Evolutionary Computation Conference, GECCO 2023
Country/TerritoryPortugal
CityLisbon
Period15/07/2319/07/23

Keywords

  • combinatorial optimization
  • local search
  • metaheuristics
  • multi-objective optimization
  • speedup technique

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