Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization

  • Wang, Peng (College of Arts and Sciences, Northeast Agricultural University) ;
  • Bai, Jiyun (College of Arts and Sciences, Northeast Agricultural University) ;
  • Meng, Jun (College of Arts and Sciences, Northeast Agricultural University)
  • Received : 2018.08.31
  • Accepted : 2020.03.12
  • Published : 2020.10.31
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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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