The travelling salesman's problem: A self-adapting PSO-ACS algorithm

David Gómez-Cabrero; Carmen Armero; D. Nalin Ranasinghe

doi:10.1109/ICIINFS.2007.4579225

The travelling salesman's problem: A self-adapting PSO-ACS algorithm

David Gómez-Cabrero, Carmen Armero, D. Nalin Ranasinghe

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

11 Scopus citations

Abstract

This paper presents a combination of two wellknown metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony System (ACS), based on a framework design named A-B-Domain. We take the T ravelling Salesman's Problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials. ©2007 IEEE.

Original language	English (US)
Title of host publication	ICIIS 2007 - 2nd International Conference on Industrial and Information Systems 2007, Conference Proceedings
Pages	479-484
Number of pages	6
DOIs	https://doi.org/10.1109/ICIINFS.2007.4579225
State	Published - Dec 1 2007
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-02-16

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10.1109/ICIINFS.2007.4579225

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title = "The travelling salesman's problem: A self-adapting PSO-ACS algorithm",

abstract = "This paper presents a combination of two wellknown metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony System (ACS), based on a framework design named A-B-Domain. We take the T ravelling Salesman's Problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials. {\textcopyright}2007 IEEE.",

author = "David G{\'o}mez-Cabrero and Carmen Armero and {Nalin Ranasinghe}, D.",

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year = "2007",

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doi = "10.1109/ICIINFS.2007.4579225",

language = "English (US)",

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T1 - The travelling salesman's problem: A self-adapting PSO-ACS algorithm

AU - Gómez-Cabrero, David

AU - Armero, Carmen

AU - Nalin Ranasinghe, D.

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16

PY - 2007/12/1

Y1 - 2007/12/1

N2 - This paper presents a combination of two wellknown metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony System (ACS), based on a framework design named A-B-Domain. We take the T ravelling Salesman's Problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials. ©2007 IEEE.

AB - This paper presents a combination of two wellknown metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony System (ACS), based on a framework design named A-B-Domain. We take the T ravelling Salesman's Problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials. ©2007 IEEE.

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U2 - 10.1109/ICIINFS.2007.4579225

DO - 10.1109/ICIINFS.2007.4579225

M3 - Conference contribution

SN - 1424411521

SP - 479

EP - 484

BT - ICIIS 2007 - 2nd International Conference on Industrial and Information Systems 2007, Conference Proceedings

ER -

The travelling salesman's problem: A self-adapting PSO-ACS algorithm

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