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Proceedings Paper

An ant colony algorithm based on differential evolution
Author(s): Mingshan Liu; Yanqin Xun; Yuan Zhou; Rui Wang; Wenbo Zhang
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Paper Abstract

In the view of solving the combinatorial optimization problems, there are some faults for Ant Colony Optimization(ACO), such as the long compution and easy to fall into local optimum. To solve these problems, the improved ACO based Differential Evolution(DETCACS) is presented. Different from other DEACO, the transforming between natural number coding and real number is applied in the path planning in the new algorithm ,so that the multiple populations differential evolution and guiding cross can be used to ensuring the diversity. Moreover ,The cross removing strategy are applied to accelerate the convergence process. At last, combined with classic Traveling Salesman Problem(TSP) instances in MATLAB, the DETCACS algorithm shows good performance.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003365 (29 August 2016); doi: 10.1117/12.2244851
Show Author Affiliations
Mingshan Liu, Jilin Univ. (China)
Yanqin Xun, Jilin Univ. (China)
Yuan Zhou, Jilin Univ. (China)
Rui Wang, Jilin Univ. (China)
Wenbo Zhang, Jilin Univ. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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