Improved robustness through population variance in ant colony optimization

David C. Matthews, Andrew M. Sutton, Doug Hains, L. Darrell Whitley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Ant Colony Optimization algorithms are population-based Stochastic Local Search algorithms that mimic the behavior of ants, simulating pheromone trails to search for solutions to combinatorial optimization problems. This paper introduces Population Variance, a novel approach to ACO algorithms that allows parameters to vary across the population over time, leading to solution construction differences that are not strictly stochastic. The increased exploration appears to help the search escape from local optima, significantly improving the robustness of the algorithm with respect to suboptimal parameter settings.

Original languageEnglish (US)
Title of host publicationEngineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics - Second International Workshop, SLS 2009, Proceedings
Pages145-149
Number of pages5
DOIs
StatePublished - Nov 4 2009
Externally publishedYes
Event2nd International Workshop on Engineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics, SLS 2009 - Brussels, Belgium
Duration: Sep 3 2009Sep 4 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5752 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Engineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics, SLS 2009
CountryBelgium
CityBrussels
Period9/3/099/4/09

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    Matthews, D. C., Sutton, A. M., Hains, D., & Whitley, L. D. (2009). Improved robustness through population variance in ant colony optimization. In Engineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics - Second International Workshop, SLS 2009, Proceedings (pp. 145-149). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5752 LNCS). https://doi.org/10.1007/978-3-642-03751-1_16