Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Jiao, Zhenghua; Zhang, Lei; Xu, Mingwei; Cai, Changxin; Xiong, Jie

doi:10.1109/access.2019.2954356

Cited by 25 publications

(19 citation statements)

References 38 publications

(44 reference statements)

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“…□ Remark 5: For the unknown non-symmetric input saturation (18), the states of the closed-loop system converge to zero in prescribed finite time using the non-singular control law (24). The term p j /q j must satisfy (5) to avoid the singularity problem in the control law (24) for s j = 0. The design parameter β 0 should be chosen as a large positive constant to guarantee the design requirement of the proposed control scheme.…”

Section: Prescribed Finite Time Disturbance Observer Using Tsmc With mentioning

confidence: 99%

“…Recently, the PSO algorithm has been successfully used to improve the performance of control schemes. Variants of PSO algorithms, such as multi‐objective control‐based PSO algorithm [22], system identification‐based PSO algorithm [23], coverage control‐based PSO algorithm [24], and fuzzy SMC‐based PSO algorithm [25] have been successfully developed for control of various systems. In this paper, the PSO algorithm is employed to achieve the design parameters of the proposed disturbance observer‐based TSMC.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

Vahidi-Moghaddam

Rajaei

Ayati

et al. 2020

IET Control Theory & Appl

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In this study, adaptive prescribed finite-time stabilisation of uncertain single-input and single-output non-linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non-symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non-singular prescribed finite time terminal sliding mode control is proposed for the closed-loop control of the system with the non-symmetric input saturation. Extended Kalman filter algorithm is employed for the real-time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non-linear vibration of a non-local strain gradient nanobeam. The Galerkin projection method is used to reduce the non-dimensional form of the governing non-linear partial differential equation of Euler-Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control.

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Section: Prescribed Finite Time Disturbance Observer Using Tsmc With mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

Vahidi-Moghaddam

Rajaei

Ayati

et al. 2020

IET Control Theory & Appl

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“…Most of the research on sensor coverage involves heuristic sensor coverage algorithms [4]. In previous studies, scholars have proposed many optimization algorithms to solve wireless sensor network (WSNs) problems, including genetic algorithm (GA) and particle swarm optimization (PSO) [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Otherwise, the individual artificial fish will prey. Equations (10) and (9) express the mathematical following behavior. Figure 4 shows the pseudocode of CPAFSA's following behavior.…”

mentioning

confidence: 99%

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Zhou

Liu

2021

Journal of Sensors

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In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

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“…In practical engineering applications, there are many constrained multi-objective optimization problems (CMOPs) [1], [2] in which multiple objectives and constraints need to be optimized. Without a loss of generality, CMOPs can be formulated as formula (1):…”

Section: Introductionmentioning

confidence: 99%

Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution

Liu

2021

IEEE Access

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To improve distribution and convergence of the obtained solution set in constrained multiobjective optimization problems, this paper presents an adaptive ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (ε-MOEA/D-DE). First, an adaptive ε-constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multiobjective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.INDEX TERMS Constrained many-objective optimization, ε-constrain handling techniques, differential evolution algorithm, MOEA/D

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Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Cited by 25 publications

References 38 publications

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution

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