An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volat...

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Bibliographic Details
Published inApplied Mechanics and Materials Vol. 389; pp. 849 - 853
Main Authors Feng, Wei, Pan, Da Zhi, Cui, Fang Song, Cheng, Guo Zhong, Yang, Shuang
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 01.08.2013
Subjects
Precocity
Ant Colony Optimization (ACO)
Stagnation
Volatility Coefficient
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Summary:In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.
Bibliography:Selected, peer reviewed papers from the International Conference on Mechatronic Systems and Materials Application (ICMSMA 2013), June 26-27, 2013, Guangzhou, China
ISBN:303785815X
9783037858158
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.389.849