Article

A discrete particle swarm optimization algorithm for the generalized traveling salesman problem

Authors:

Mehmet Fatih Tasgetiren,

P. N. Suganthan, and

Quan-Qe PanAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

Pages 158 - 167

https://doi.org/10.1145/1276958.1276980

Published: 07 July 2007 Publication History

Abstract

Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.

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Index Terms

A discrete particle swarm optimization algorithm for the generalized traveling salesman problem
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Pham TRaahemi B(2023)Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2023.327255611(43733-43758)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3272556
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https://doi.org/10.1007/s10472-023-09834-5
Schmidt JIrnich S(2022)New Neighborhoods and an Iterated Local Search Algorithm for the Generalized Traveling Salesman ProblemEURO Journal on Computational Optimization10.1016/j.ejco.2022.100029(100029)Online publication date: May-2022
https://doi.org/10.1016/j.ejco.2022.100029
Cosma OPop PCosma L(2021)An Effective Hybrid Genetic Algorithm for Solving the Generalized Traveling Salesman ProblemHybrid Artificial Intelligent Systems10.1007/978-3-030-86271-8_10(113-123)Online publication date: 15-Sep-2021
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Kumar AKumar T(2017)Materialized view selection using discrete genetic operators based particle swarm optimization2017 International Conference on Inventive Systems and Control (ICISC)10.1109/ICISC.2017.8068675(1-5)Online publication date: Jan-2017
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