D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity

Qingquan ZHANG, Feng WU, Yang TAO, Jiyuan PEI, Jialin LIU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size. © 2020 IEEE.
Original languageEnglish
Title of host publication2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2754-2761
Number of pages8
ISBN (Print)9781728125473
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

Keywords

  • Capacitated arc routing problem
  • local search
  • memetic algorithms
  • meta-heuristics
  • multiobjective optimization

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