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A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Author

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  • FuTao Zhao
  • Zhong Yao
  • Jing Luan
  • Xin Song

Abstract

A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

Suggested Citation

  • FuTao Zhao & Zhong Yao & Jing Luan & Xin Song, 2016. "A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:2167413
    DOI: 10.1155/2016/2167413
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    Cited by:

    1. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    2. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).

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