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

Yang Zhang, Huihui Zhao, Yuming Cao, Qinhuo Liu, Zhanfeng Shen, Jian Wang and Minggang Hu
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Yang Zhang: North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Beijing 100120, 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

Energies, 2018, vol. 11, issue 10, 1-23

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.

Keywords: heating engineering; route planning; ant colony optimization; cuckoo search; hybrid intelligent algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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