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

Peng Wang|Jiyun Bai|Jun Meng
한국정보처리학회|2020.10|JIPS(Journal of Information Processing Systems) (Journal of Information Processing Systems)|제16권 제5호|pp. 1169-1182|DOI 10.3745/JIPS.01.0059

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참고문헌

키워드

Genetic AlgorithmAnt Colony AlgorithmCloud Model

초록

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. Thegenetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and theoptimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods anddiscussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved betterperformance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reachthe global optimal solution.

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. Thegenetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and theoptimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods anddiscussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved betterperformance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reachthe global optimal solution.

참고문헌 (39)

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