Bioinspired Discrete Two-Stage Surrogate-Assisted Algorithm for Large-Scale Traveling Salesman Problem

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260602" target="_blank" >RIV/61989100:27240/25:10260602 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s42235-025-00724-6" target="_blank" >https://link.springer.com/article/10.1007/s42235-025-00724-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42235-025-00724-6" target="_blank" >10.1007/s42235-025-00724-6</a>

Result language

angličtina

Original language name

Bioinspired Discrete Two-Stage Surrogate-Assisted Algorithm for Large-Scale Traveling Salesman Problem

Original language description

The Traveling Salesman Problem (TSP) is a well-known NP-Hard problem, particularly challenging for conventional solving methods due to the curse of dimensionality in high-dimensional instances. This paper proposes a novel Double-stage Surrogate-assisted Pigeon-inspired Optimization algorithm (DOSA-PIO) to address this issue. DOSA-PIO integrates the ordering points to identify the clustering structure method for data clustering and employs a local surrogate model to assist the evolution of the Pigeon-inspired Optimization (PIO) algorithm. This combination enhances the algorithm&apos;s ability to explore the solution space and converge to optimal solutions more effectively. Additionally, two novel approaches are introduced to extend the generalizability of continuous algorithms for solving discrete problems, enabling the adaptation of continuous optimization techniques to the discrete nature of TSP. Extensive experiments using benchmark functions and high-dimensional TSP instances demonstrate that DOSA-PIO significantly outperforms comparative algorithms in various dimensions (10D, 20D, 30D, 50D, and 100D). The proposed algorithm provides superior solutions compared to traditional methods, highlighting its potential for solving high-dimensional TSPs. By leveraging advanced data clustering techniques and surrogate-assisted optimization, DOSA-PIO offers an effective solution for high-dimensional TSP instances, with experimental results confirming its superior performance and potential for practical applications in complex optimization problems.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20202 - Communication engineering and systems

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Bionic Engineering

ISSN

1672-6529

e-ISSN

2543-2141

Volume of the periodical

22

Issue of the periodical within the volume

4

Country of publishing house

SG - SINGAPORE

Number of pages

14

Pages from-to

nestránkováno

UT code for WoS article

001503291000001

EID of the result in the Scopus database

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