Solving network synthesis problems using ant colony optimisation

Marcus Randall, Elliot Tonkes

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

4 Citations (Scopus)

Abstract

Ant colony optimisation is a relatively new meta-heuristic search technique for solving optimisation problems. To date, much research has concentrated on solving standard benchmark problems such as the travelling salesman problem, quadratic assignment problem and the job sequencing problem. In this paper, we investigate the application of ant colony optimisation to practical telecommunication design and synthesis problems having real-world constraints. We consider a modelling approach suitable for ant colony optimisation implementation and compare the results to the simulated annealing meta-heuristic.

Original languageEnglish
Title of host publicationEngineering of Intelligent Systems
Subtitle of host publication IEA/AIE 2001
EditorsL Monostori, J Vancza, M Ali
Place of PublicationBerlin
PublisherSpringer
Pages1-10
Number of pages10
Volume2070
ISBN (Print)3540422196, 9783540422198
DOIs
Publication statusPublished - 2001
Event14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001 - Budapest, Hungary
Duration: 4 Jun 20017 Jun 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2070
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001
CountryHungary
CityBudapest
Period4/06/017/06/01

Fingerprint

Ant colony optimization
Synthesis
Metaheuristics
Quadratic Assignment Problem
Traveling salesman problem
Heuristic Search
Travelling salesman problems
Simulated annealing
Telecommunications
Simulated Annealing
Sequencing
Telecommunication
Benchmark
Optimization Problem
Modeling

Cite this

Randall, M., & Tonkes, E. (2001). Solving network synthesis problems using ant colony optimisation. In L. Monostori, J. Vancza, & M. Ali (Eds.), Engineering of Intelligent Systems: IEA/AIE 2001 (Vol. 2070, pp. 1-10). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2070). Berlin: Springer. https://doi.org/10.1007/3-540-45517-5_1
Randall, Marcus ; Tonkes, Elliot. / Solving network synthesis problems using ant colony optimisation. Engineering of Intelligent Systems: IEA/AIE 2001. editor / L Monostori ; J Vancza ; M Ali. Vol. 2070 Berlin : Springer, 2001. pp. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Randall, M & Tonkes, E 2001, Solving network synthesis problems using ant colony optimisation. in L Monostori, J Vancza & M Ali (eds), Engineering of Intelligent Systems: IEA/AIE 2001. vol. 2070, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2070, Springer, Berlin, pp. 1-10, 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001, Budapest, Hungary, 4/06/01. https://doi.org/10.1007/3-540-45517-5_1

Solving network synthesis problems using ant colony optimisation. / Randall, Marcus; Tonkes, Elliot.

Engineering of Intelligent Systems: IEA/AIE 2001. ed. / L Monostori; J Vancza; M Ali. Vol. 2070 Berlin : Springer, 2001. p. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2070).

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

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Randall M, Tonkes E. Solving network synthesis problems using ant colony optimisation. In Monostori L, Vancza J, Ali M, editors, Engineering of Intelligent Systems: IEA/AIE 2001. Vol. 2070. Berlin: Springer. 2001. p. 1-10. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-45517-5_1