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Energies 2018, 11(10), 2675; https://doi.org/10.3390/en11102675

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

1
North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Beijing 100120, China
2
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
*
Authors to whom correspondence should be addressed.
Received: 19 September 2018 / Revised: 27 September 2018 / Accepted: 3 October 2018 / Published: 8 October 2018
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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. View Full-Text
Keywords: heating engineering; route planning; ant colony optimization; cuckoo search; hybrid intelligent algorithm heating engineering; route planning; ant colony optimization; cuckoo search; hybrid intelligent algorithm
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Zhang, Y.; Zhao, H.; Cao, Y.; Liu, Q.; Shen, Z.; Wang, J.; Hu, M. A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering. Energies 2018, 11, 2675.

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