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

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.

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“…The ACO algorithm applies the pheromone update rule procedure, where an ant is a simple computational agent that interactively builds a solution to the problem. For each interaction performed by the algorithm, each ant moves from one status r to another status s, obtaining a more complete intermediate solution [26], [27]. The k th ant from state r to state s is selected among the unvisited states memorized in J k r :…”

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“…From the references aforementioned and our previous study on the DH route optimization [12], we can draw that the AS is the first ACO algorithm to be proposed in the literature. Meanwhile, the ACS is built on the AS and outperforms AS in three main aspects: (i) the state transition rule, (ii) the global updating rule, and (iii) the local pheromone updating rule [16].…”

“…Most of them have been verified to solve the traveling salesman problem (TSP), which is an abstract path-finding problem [11]. For the DH route optimization problem, Zhang et al [12] modified the ACO algorithm and the CS algorithm, and proposed a hybrid algorithm that combined the two algorithms. They applied the algorithms to the Zhuozhou-Fangshan heating engineering project, and found that the modified ACO algorithm had better convergence and was more stable, but spent more computation time.…”

“…There are also various studies on the hybridization of ACO with other intelligent algorithms. Zhang et al combined ACO with the CS algorithm to solve the heating route optimization problem [12]. Mahi et al proposed a new hybrid method to optimize parameters using particle swarm optimization (PSO).…”

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“…Ant Colony Optimization (ACO) can be used in optimization of biodiesel production [65], in AC/DC distribution network planning problem [66], etc. Cuckoo Search Algorithm (CSA) usage is found in the analysis and optimization of magnetic levitation system [67], in route optimization of heating engineering [68] as well as in research and application of hybrid wind-energy forecasting models [69]. Hybrid Genetic Algorithm (HGA) is improved version of classic GA, which also can be used in resolving many problems of energy systems.…”