Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

Zhao, Qiang; Li, Changwei; Zhu, Daiyin; Xie, Chunli

doi:10.3390/electronics11060853

Cited by 25 publications

(4 citation statements)

References 43 publications

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“…The conventional PSO algorithm exhibits the issue of premature convergence to local optima, which may affect its optimization performance in solving complex optimization problems [ 26 ]. In this regard, the introduction of chaos theory into the PSO algorithm can improve the optimization performance, by modifying the PSO algorithm to escape more easily from local optima [ 30 , 64 , 65 ]. Therefore, in this paper, the logistic chaos map is implemented to dynamically adjust the value of inertia weight, in order to maintain the appropriate balance between global exploration and local exploitation abilities during the optimization process.…”

Section: Particle Swarm Optimization and The Proposed Improved Versionmentioning

confidence: 99%

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Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

Rosic

Sedak

Simić

et al. 2022

Sensors

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This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).

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Section: Particle Swarm Optimization and The Proposed Improved Versionmentioning

confidence: 99%

“…In recent years, the introduction of chaos theory is emerging as a powerful approach for improving the optimization performance of different metaheuristic algorithms [ 64 , 65 ]. Chaos is a bounded dynamic behavior that can be observed in certain nonlinear dynamic systems.…”

Section: Particle Swarm Optimization and The Proposed Improved Versionmentioning

confidence: 99%

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

Rosic

Sedak

Simić

et al. 2022

Sensors

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“…Based on a modified hybrid strategy weed algorithm, a method for optimizing coverage is presented. Q Zhao et al suggests an optimization technique for WSN coverage optimization that blends particle swarm optimization with chaos optimization 8 .…”

Section: Introductionmentioning

confidence: 99%

Coverage optimization in wireless sensor networks with a modified arithmetic optimization algorithm

Kong¹,

Qian²,

Xu³

2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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A coverage optimization method is suggested to improved node coverage in wireless sensor networks (WSNs), decrease node redundancy, and lengthen network lifetime, this method is based on the adaptive grey wolf arithmetic optimization algorithm The exploitation and exploration are first balanced using a cosine acceleration function. Then, to boost the method's performance, the grey wolf optimizer and arithmetic optimization algorithm are combined. In comparison to the conventional coverage optimization algorithm, our simulation results could improve arithmetic optimization algorithm has higher convergence efficiency and coverage rate, decreases network loss, and significantly lengthens the network life cycle.

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“…The particle swarm algorithm, proposed by Kennedy and Eberhart in 1995 [10], is widely used to solve various engineering problems because of its fast convergence speed, ease of implementation, and few parameters for simple modeling [11][12][13][14]. However, it also has defects such as precocious puberty, low precision, and easily falling into local optimization.…”

Section: Introductionmentioning

confidence: 99%

A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism

Liu

Wang

et al. 2022

Electronics

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Aiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mechanism. When the population falls into local optimal or premature convergence, the restart strategy is activated to expand the search range by re-randomly initializing the group particles. An inverted S-type decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the ability of local search and global search. Finally, the new RDS-PSO algorithm is combined with cubic spline interpolation to apply to the path planning and smoothing processing of mobile robots, and the coding mode based on the path node as a particle individual is constructed, and the penalty function is selected as the fitness function to solve the shortest collision-free path. The comparative results of simulation experiments show that the RDS-PSO algorithm proposed in this paper solves the problem of falling into local extremums and precocious puberty, significantly improves the optimization, speed, and effectiveness of the path, and the simulation experiments in different environments also show that the algorithm has good robustness and generalization.

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Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

Cited by 25 publications

References 43 publications

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

Coverage optimization in wireless sensor networks with a modified arithmetic optimization algorithm

A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism

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