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A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

Author

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  • Yang Zhang

    (North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Beijing 100120, China
    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Huihui Zhao

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Yuming Cao

    (North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Beijing 100120, China)

  • Qinhuo Liu

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Zhanfeng Shen

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Jian Wang

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Minggang Hu

    (North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Beijing 100120, China)

Abstract

The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

Suggested Citation

  • Yang Zhang & Huihui Zhao & Yuming Cao & Qinhuo Liu & Zhanfeng Shen & Jian Wang & Minggang Hu, 2018. "A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering," Energies, MDPI, vol. 11(10), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2675-:d:174234
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    References listed on IDEAS

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    1. Yu, Bin & Yang, Zhong-Zhen & Yao, Baozhen, 2009. "An improved ant colony optimization for vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 196(1), pages 171-176, July.
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    2. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.

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