An Improved Particle Swarm Optimization Algorithm for Wireless Sensor Networks Localization

Hu, Xinyi; Shi, Shuo; Gu, Xuemai

doi:10.1109/wicom.2012.6478418

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…By using particles to imitate the estimated coordinates of unknown nodes, some methods model the localization problem as a single-objective optimization model with the space distance constraint as the only fitness function. For example, the PSO localization algorithm based on log-barrier constraint function could accelerate the convergence speed and save energy [4], the PSO localization adopting crossover operator and the mutation operator could avoid the premature convergence [5], and the PSO localization algorithm based on quantum mechanics could enhance the global convergence and improve the accuracy [6]. However, it always happens that the results of estimated nodes’ localizations meet the space distance constraint without meeting the geometric topology constraint because of ranging errors in some practical applications.…”

Section: Introductionmentioning

confidence: 99%

Localization Algorithm in Wireless Sensor Networks Based on Multiobjective Particle Swarm Optimization

Sun

Wang

et al. 2015

International Journal of Distributed Sensor Networks

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Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain the archive in order to limit the size of archive, and the global optimum is obtained according to the proportion of selection. The simulation results show considerable improvements in terms of localization accuracy and convergence rate while keeping a limited archive size by a method using the global optimal selection operator and dynamically maintaining the archive.

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Section: Introductionmentioning

confidence: 99%

Localization Algorithm in Wireless Sensor Networks Based on Multiobjective Particle Swarm Optimization

Sun

Wang

et al. 2015

International Journal of Distributed Sensor Networks

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“…The inertia weight for every particle is dynamically updated based on the feedback taken from the fitness of the best previous position found by the particle, and a novel methodology is incorporated into the novel particle swarm optimization to be able to effectively response and detect any parameter variations of system to be identified. Hu and Shi [16], to solve the premature convergence problem of PSO, improved algorithms with hybrid and mutation operators, leading to obtaining a high level of particle population diversity, decreasing the possibility of falling into local optima, and improving location accuracy. The novel algorithm is introduced in the rangebased location for wireless sensor networks and simulation shows a better performance than basic PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

Yan

Deng

et al. 2014

Mathematical Problems in Engineering

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A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponential inertia weight particle swarm optimization(EPSO), andtime-varying acceleration coefficient(TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

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“…A neural network approach was implemented in [10]. Algorithms based on PSO or variants were proposed in [11][12][13]. Combining the advantages of PSO and genetic algorithm (GA), an improved FPSO + FGA hybrid method was provided in [14].…”

mentioning

confidence: 99%

EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things

Cheng

Qin

et al. 2018

Int. J. Onl. Eng.

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Abstract-Internet of Things (IOT) has found broad applications and has drawn more and more attention from researchers. At the same time, IOT also presents many challenges, one of which is node localization, i.e. how to determine the geographical position of each sensor node. Algorithms have been proposed to solve the problem. A popular algorithm is Particle Swarm Optimization (PSO) because it is simple to implement and needs relatively less computation. However, PSO is easily trapped into local optima and gives premature results. In order to improve the PSO algorithm, this paper proposes the EHPSO algorithm based on Novel Particle Swarm Optimization (NPSO) and Hybrid Particle Swarm Optimization (HPSO). The EHPSO algorithm applies the principle of best neighbor of each particle to the HPSO algorithm. Simulation results indicate that EHPSO outperforms HPSO and NPSO in evaluating accurate node positions and improves convergence by avoiding being trapped into local optima.

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An Improved Particle Swarm Optimization Algorithm for Wireless Sensor Networks Localization

Cited by 5 publications

References 5 publications

Localization Algorithm in Wireless Sensor Networks Based on Multiobjective Particle Swarm Optimization

Localization Algorithm in Wireless Sensor Networks Based on Multiobjective Particle Swarm Optimization

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things

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