Hybrid Ant Colony and Cuckoo Search Algorithm for Intelligent Routing in Wireless Sensor Network

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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Multiagent based intelligent routing in wireless sensor network

2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs.2017.8014565 ◽

2017 ◽

Author(s):

R. Suresh ◽

M. Somasundaram ◽

K. L. Shunmuganathan

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Wireless Sensor ◽

Intelligent Routing

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

Energies ◽

10.3390/en11102675 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2675 ◽

Cited By ~ 4

Author(s):

Yang Zhang ◽

Huihui Zhao ◽

Yuming Cao ◽

Qinhuo Liu ◽

Zhanfeng Shen ◽

...

Keyword(s):

Ant Colony Optimization ◽

Optimization Algorithm ◽

Search Algorithm ◽

Fitness Function ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Elapsed Time ◽

Route Design

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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