Evolutionary approaches with adaptive operators for the bi-objective TTP

Roberto Santana, Sidhartha Shakya

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

Abstract

One characteristic feature of the traveling thief problem (TTP) is the existence of different facets of instance difficulty that make no single optimization algorithm to excel over the others. Multi-objective variants of the TTP add to the challenging features of the single-objective TTP the difficulties associated to keep a set of diverse non-dominated solutions. In this paper we propose an adaptive hybrid TTP optimization algorithm based on the probabilistic application of variation operators. The algorithm combines variation operators conceived for different facets of instance difficulty and adapts their frequency of application to the characteristics of the instances. The introduced algorithm shows a robust behavior across different bi-objective TTP instances producing results competitive with those achieved by state of the art algorithms.

Original languageBritish English
Title of host publicationProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
EditorsHisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1202-1209
Number of pages8
ISBN (Electronic)9781665487689
DOIs
StatePublished - 2022
Event2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore
Duration: 4 Dec 20227 Dec 2022

Publication series

NameProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/TerritorySingapore
CitySingapore
Period4/12/227/12/22

Keywords

  • dynamic programming
  • evolutionary optimization
  • MOEA
  • traveling thief problem
  • TSP

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