Multi-area Path Planning for Wireless Sensor Networks Based on Double Populations Ant Colony Optimization Algorithm

Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different...

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Veröffentlicht in:2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) S. 152 - 159
Hauptverfasser: Zhai, Chenxuan, Wang, Minghua, Jiang, Kaiwu, Wang, Yan, Fan, Bo, Wang, Chao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2020
Schlagworte:
Ant Colony Optimization algorithms(ACO)
Convergence
Double Populations Ant Colony Optimization algorithms(DPACO)
Electrical engineering
Heuristic algorithms
Path planning
Sociology
Statistics
Wireless sensor networks
Wireless Sensor Networks(WSNs)
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Zusammenfassung:Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different obstacle environments. Second, to improve the efficiency of ant search in the early stages set a non-uniform initial pheromone concentration, and introduce new populations to expand the search space of the algorithm and avoid the algorithm falling into a local optimum. Finally, it is proposed that the elite ant pheromone update principle and adaptively adjusts the volatile coefficients to ensure the global search capability and improve the convergence speed of the algorithm. The experimental results show that the algorithm has a high global search capability and significantly faster convergence speed, which verifies the effectiveness and superiority of the algorithm.
DOI:10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00041