A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization


Peng Wang, Jiyun Bai, Jun Meng, Journal of Information Processing Systems Vol. 16, No. 5, pp. 1169-1182, Oct. 2020  

10.3745/JIPS.01.0059
Keywords: Ant Colony Algorithm, Cloud Model, genetic algorithm
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Abstract

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.


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Cite this article
[APA Style]
Peng Wang, Jiyun Bai, & Jun Meng (2020). A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization. Journal of Information Processing Systems, 16(5), 1169-1182. DOI: 10.3745/JIPS.01.0059.

[IEEE Style]
P. Wang, J. Bai and J. Meng, "A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1169-1182, 2020. DOI: 10.3745/JIPS.01.0059.

[ACM Style]
Peng Wang, Jiyun Bai, and Jun Meng. 2020. A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization. Journal of Information Processing Systems, 16, 5, (2020), 1169-1182. DOI: 10.3745/JIPS.01.0059.