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An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

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ABSTRACT

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

No MeSH data available.


Pseudocode for VFCPSO algorithm.
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f4-sensors-07-00354: Pseudocode for VFCPSO algorithm.

Mentions: Similar to the VFPSO algorithm, the virtual force directed co-evolutionary particle swarm optimization (VFCPSO) algorithm combines the hybrid CPSO with VF for dynamic deployment in WSNs. In VFCPSO, the global search of optimal deployment is achieved by the hybrid CPSO algorithm in a co-evolutionary manner for improving the solution quality and robustness. The pseudocode for VFCPSO is illustrated in Figure 4, where Q is a normal n-dimensional swarm, Q.xk presents its current position of particle k, Q.yk is the local best position of particle k, Q. ŷ is the global best solution of swarm Q.


An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment
Pseudocode for VFCPSO algorithm.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756725&req=5

f4-sensors-07-00354: Pseudocode for VFCPSO algorithm.
Mentions: Similar to the VFPSO algorithm, the virtual force directed co-evolutionary particle swarm optimization (VFCPSO) algorithm combines the hybrid CPSO with VF for dynamic deployment in WSNs. In VFCPSO, the global search of optimal deployment is achieved by the hybrid CPSO algorithm in a co-evolutionary manner for improving the solution quality and robustness. The pseudocode for VFCPSO is illustrated in Figure 4, where Q is a normal n-dimensional swarm, Q.xk presents its current position of particle k, Q.yk is the local best position of particle k, Q. ŷ is the global best solution of swarm Q.

View Article: PubMed Central

ABSTRACT

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

No MeSH data available.