Runtime analysis of ant colony optimization on dynamic shortest path problems

Andrei Lissovoi; Carsten Witt

doi:10.1145/2463372.2463567

Runtime analysis of ant colony optimization on dynamic shortest path problems

Andrei Lissovoi, Carsten Witt

Department of Applied Mathematics and Computer Science

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

Abstract

A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\lambda$ ants per vertex helps in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic shortest-path problems which make the optimum difficult to track are discussed. Experiments illustrate theoretical findings and conjectures.

Original language	English
Title of host publication	Proceeding of the fifteenth annual conference on Genetic and evolutionary computation
Publisher	Association for Computing Machinery
Publication date	2013
Pages	1605-1612
ISBN (Print)	978-1-4503-1963-8
DOIs	https://doi.org/10.1145/2463372.2463567
Publication status	Published - 2013
Event	2013 Genetic and Evolutionary Computation Conference - Amsterdam, Netherlands Duration: 6 Jul 2013 → 10 Jul 2013 http://www.sigevo.org/gecco-2013/

Conference

Conference	2013 Genetic and Evolutionary Computation Conference
Country/Territory	Netherlands
City	Amsterdam
Period	06/07/2013 → 10/07/2013
Internet address	http://www.sigevo.org/gecco-2013/

Keywords

Ant Colony Optimization
Shortest Paths
Dynamic Problems
Runtime Analysis

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10.1145/2463372.2463567

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Cite this

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title = "Runtime analysis of ant colony optimization on dynamic shortest path problems",

abstract = "A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\lambda$ ants per vertex helps in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic shortest-path problems which make the optimum difficult to track are discussed. Experiments illustrate theoretical findings and conjectures.",

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Lissovoi, A & Witt, C 2013, Runtime analysis of ant colony optimization on dynamic shortest path problems. in Proceeding of the fifteenth annual conference on Genetic and evolutionary computation. Association for Computing Machinery, pp. 1605-1612 , 2013 Genetic and Evolutionary Computation Conference, Amsterdam, Netherlands, 06/07/2013. https://doi.org/10.1145/2463372.2463567

Runtime analysis of ant colony optimization on dynamic shortest path problems. / Lissovoi, Andrei; Witt, Carsten.
Proceeding of the fifteenth annual conference on Genetic and evolutionary computation. Association for Computing Machinery, 2013. p. 1605-1612 .

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

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KW - Dynamic Problems

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Runtime analysis of ant colony optimization on dynamic shortest path problems

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