29 August 2016 An ant colony algorithm based on differential evolution
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003365 (2016) https://doi.org/10.1117/12.2244851
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
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.
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Mingshan Liu, Mingshan Liu, Yanqin Xun, Yanqin Xun, Yuan Zhou, Yuan Zhou, Rui Wang, Rui Wang, Wenbo Zhang, Wenbo Zhang, } "An ant colony algorithm based on differential evolution", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003365 (29 August 2016); doi: 10.1117/12.2244851; https://doi.org/10.1117/12.2244851
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