Ant Colony Optimization with Local Search for Continuous Functions

Cheng Ming Qi

doi:10.4028/www.scientific.net/AMR.204-210.1135

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 204-210Ant Colony Optimization with Local Search for...

Ant Colony Optimization with Local Search for Continuous Functions

Abstract:

Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.

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Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

1135-1138

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.204-210.1135

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Online since:

February 2011

Authors:

Cheng Ming Qi

Keywords:

Ant Colony System, Continuous Functions, Pareto Local Search

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References

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