Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

•We propose an improved culture algorithm–ant colony algorithm to solve the problem of nodes deployment.•The search for optimal solution in our algorithm becomes much better and more stable.•A new convergence judging method is used to achieve the purpose of global optimization.•Extensive simulation...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 250; pp. 58 - 70
Main Authors: Sun, Xuemei, Zhang, Yiming, Ren, Xu, Chen, Ke
Format: Journal Article
Language:English
Published: Elsevier Inc 01.01.2015
Subjects:
Algorithms
Ant colony algorithm
Connectivity
Coverage
Culture
Culture algorithm
Evolution
Networks
Nodes deployment
Optimization
Remote sensors
Searching
Strategy
Wireless networks
Wireless sensor networks
Wireless sensor networks
Connectivity
Coverage
Nodes deployment
Culture algorithm
Ant colony algorithm
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:•We propose an improved culture algorithm–ant colony algorithm to solve the problem of nodes deployment.•The search for optimal solution in our algorithm becomes much better and more stable.•A new convergence judging method is used to achieve the purpose of global optimization.•Extensive simulation experiments have been conducted to verify the effectiveness of our CA–ACA algorithm. The optimization of nodes deployment is one of the most active research areas in wireless sensor networks. In this paper, we propose an improved culture algorithm–ant colony algorithm (CA–ACA) to solve the problem of nodes deployment. Double evolution mechanism of culture algorithm is integrated into the improved ant colony optimization algorithm within the population space as an evolutionary strategy, and then directs the search of population space through the elites of continuous evolution in belief space. The introduction of culture algorithm makes the search for optimization faster and better stability of CA–ACA than traditional ones. In addition, greedy strategy is introduced for the situation of sparsely monitored points, which makes CA–ACA be suitable for any environment. Furthermore, we also investigate the convergence judging method which makes CA–ACA avoid premature convergence so as to achieve the purpose of global optimization. A large number of simulation experiments have been conducted and the results not only demonstrate the validity of CA–ACA, but also verify that CA–ACA algorithm can optimize the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. Current results are of great significance to effectively design the optimal deployment of nodes in wireless and mobile sensor networks.
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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2014.10.091