Evolutionary approaches with adaptive operators for the bi-objective TTP

Roberto Santana; Sidhartha Shakya

doi:10.1109/SSCI51031.2022.10022162

Evolutionary approaches with adaptive operators for the bi-objective TTP

Roberto Santana, Sidhartha Shakya

Emirates ICT Innovation Center

University of the Basque Country (UPV/EHU)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	British English
Title of host publication	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1202-1209
Number of pages	8
ISBN (Electronic)	9781665487689
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022162
State	Published - 2022
Event	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore Duration: 4 Dec 2022 → 7 Dec 2022

Publication series

Name	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/Territory	Singapore
City	Singapore
Period	4/12/22 → 7/12/22

Keywords

dynamic programming
evolutionary optimization
MOEA
traveling thief problem
TSP

Access to Document

10.1109/SSCI51031.2022.10022162

Cite this

Santana, R., & Shakya, S. (2022). Evolutionary approaches with adaptive operators for the bi-objective TTP. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 1202-1209). (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI51031.2022.10022162

Santana, Roberto ; Shakya, Sidhartha. / Evolutionary approaches with adaptive operators for the bi-objective TTP. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1202-1209 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

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title = "Evolutionary approaches with adaptive operators for the bi-objective TTP",

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.",

keywords = "dynamic programming, evolutionary optimization, MOEA, traveling thief problem, TSP",

author = "Roberto Santana and Sidhartha Shakya",

note = "Funding Information: R. Santana acknowledges support by the Basque Government (KK2020/00049 project through ELKARTEK program) and the Spanish Ministry of Science, Innovation and Universities (project PID2019-104966GB-I00). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 ; Conference date: 04-12-2022 Through 07-12-2022",

year = "2022",

doi = "10.1109/SSCI51031.2022.10022162",

language = "British English",

series = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1202--1209",

editor = "Hisao Ishibuchi and Chee-Keong Kwoh and Ah-Hwee Tan and Dipti Srinivasan and Chunyan Miao and Anupam Trivedi and Keeley Crockett",

booktitle = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

address = "United States",

}

Santana, R & Shakya, S 2022, Evolutionary approaches with adaptive operators for the bi-objective TTP. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 1202-1209, 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, Singapore, 4/12/22. https://doi.org/10.1109/SSCI51031.2022.10022162

Evolutionary approaches with adaptive operators for the bi-objective TTP. / Santana, Roberto; Shakya, Sidhartha.
Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1202-1209 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Santana, Roberto

AU - Shakya, Sidhartha

N1 - Funding Information: R. Santana acknowledges support by the Basque Government (KK2020/00049 project through ELKARTEK program) and the Spanish Ministry of Science, Innovation and Universities (project PID2019-104966GB-I00). Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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KW - evolutionary optimization

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T3 - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

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BT - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

A2 - Ishibuchi, Hisao

A2 - Kwoh, Chee-Keong

A2 - Tan, Ah-Hwee

A2 - Srinivasan, Dipti

A2 - Miao, Chunyan

A2 - Trivedi, Anupam

A2 - Crockett, Keeley

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Y2 - 4 December 2022 through 7 December 2022

ER -

Santana R, Shakya S. Evolutionary approaches with adaptive operators for the bi-objective TTP. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1202-1209. (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). doi: 10.1109/SSCI51031.2022.10022162

Evolutionary approaches with adaptive operators for the bi-objective TTP

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