Heuristics for ant colony optimisation using the generalised assignment problem

Marcus Randall

doi:10.1109/CEC.2004.1331130

Heuristics for ant colony optimisation using the generalised assignment problem

Marcus Randall^*

^*Corresponding author for this work

Bond Business School

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

16 Citations (Scopus)

Abstract

The use of embedded heuristics within meta-heuristic search algorithms has a large effect on their performance. One of the more recent classes of meta-heuristics, ant colony optimisation, is examined in terms of both the heuristic used to select solution components and the local search heuristics used to improve solutions. Static and adaptive heuristic control strategies are developed, as well as neighbourhood oriented local search transition operators, that are able to obtain good solutions to large and tightly constrained generalised assignment problem instances.

Original language	English
Title of host publication	Congress on Evolutionary Computation
Subtitle of host publication	CEC2004
Pages	1916-1923
Number of pages	8
Volume	2
DOIs	https://doi.org/10.1109/CEC.2004.1331130
Publication status	Published - 2004
Event	IEEE Congress on Evolutionary Computation - Portland, OR, United States Duration: 19 Jun 2004 → 23 Jun 2004 https://www.ieee.org/conferences_events/index.html

Conference

Conference	IEEE Congress on Evolutionary Computation
Abbreviated title	CEC2004
Country/Territory	United States
City	Portland, OR
Period	19/06/04 → 23/06/04
Internet address	https://www.ieee.org/conferences_events/index.html

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10.1109/CEC.2004.1331130

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title = "Heuristics for ant colony optimisation using the generalised assignment problem",

abstract = "The use of embedded heuristics within meta-heuristic search algorithms has a large effect on their performance. One of the more recent classes of meta-heuristics, ant colony optimisation, is examined in terms of both the heuristic used to select solution components and the local search heuristics used to improve solutions. Static and adaptive heuristic control strategies are developed, as well as neighbourhood oriented local search transition operators, that are able to obtain good solutions to large and tightly constrained generalised assignment problem instances.",

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AB - The use of embedded heuristics within meta-heuristic search algorithms has a large effect on their performance. One of the more recent classes of meta-heuristics, ant colony optimisation, is examined in terms of both the heuristic used to select solution components and the local search heuristics used to improve solutions. Static and adaptive heuristic control strategies are developed, as well as neighbourhood oriented local search transition operators, that are able to obtain good solutions to large and tightly constrained generalised assignment problem instances.

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U2 - 10.1109/CEC.2004.1331130

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AN - SCOPUS:4344608505

SN - 0780385152

SN - 9780780385153

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BT - Congress on Evolutionary Computation

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ER -

Heuristics for ant colony optimisation using the generalised assignment problem

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