Distributed resource allocation optimisation algorithm based on particle swarm optimisation in wireless sensor network

Hao, Xiaochen; Yao, Ning; Wang, Jiaojiao; Wang, Liyuan

doi:10.1049/iet-com.2020.0368

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…PSO's simplicity and robustness have led to its wide application in various fields, including wireless communications and image processing. It has proven effective in solving diverse optimization problems [40]- [55].…”

Section: A Related Workmentioning

confidence: 99%

TPPSO: A Novel Two-Phase Particle Swarm Optimization

Shami,

Summakieh,

Alswaitti

et al. 2023

JOIV : Int. J. Inform. Visualization

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Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems.

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Section: A Related Workmentioning

confidence: 99%

TPPSO: A Novel Two-Phase Particle Swarm Optimization

Shami,

Summakieh,

Alswaitti

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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A Novel Sensor Task Allocation Method Based on Quantum Elite Shuffled Frog Leaping Algorithm in IWSNs

Xiao

Zhang

Zhou

et al. 2021

J. Phys.: Conf. Ser.

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Maximizing the efficiency of sensor task allocation has long been a question of great interest in industrial wireless sensor networks (IWSNs). In IWSNs, different tasks performed by different sensors produce varied benefit values. The purpose of this paper is to obtain the optimal task allocation scheme of IWSNs. Therefore, we design a sensor task allocation model, and propose a quantum elite shuffled frog leaping algorithm (QES-FLA) for optimizing the task allocation in IWSNs. The proposed algorithm combines the quantum operator and elite operator to achieve better performance. By using the concept of quantum probability amplitude and quantum revolving gate, the algorithm can search the solution space in parallel, thus enhancing the efficiency of solving the task allocation problem in IWSNs. In addition, the elite operator keeps the optimal individual in the population, which ensures the performance of the algorithm. Subsequently, the proposed algorithm is compared with two other popular heuristic algorithms to make the conclusion more convincing. According to the simulation results, the algorithm we proposed has higher task benefits and better performance, thus it successfully solves the sensor task allocation problem in IWSNs.

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A comprehensive evaluation method of heat source tower heat pump applicability considering regional climate differences

Chen,

Wang,

Zhong

et al. 2023

JCM

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The heat source tower heat pump system is widely used in large and medium-sized air conditioning systems due to its good energy-saving advantages. However, there is no relatively reasonable evaluation system for the applicability of heat source tower heat pumps due to significant regional climate differences. Therefore, in order to better comprehensively evaluate the applicability of the heat source tower heat pump system, a comprehensive evaluation index system for the applicability of the heat source tower heat pump system was first constructed. On the basis of this evaluation index, an applicability evaluation model based on backpropagation neural network is constructed. In response to the slow convergence speed and susceptibility to local values in the application process of this evaluation model, particle swarm optimization algorithm is used to improve it. A comprehensive evaluation model for the applicability of heat source tower heat pumps based on improved backpropagation neural networks has been constructed. For the evaluation model constructed in the study, experimental data from four different regions were selected for validation. The experimental results show that in the training set, the F-Measure value of the evaluation model reaches 0.949, and in the test set, the F-Measure value of the model reaches 0.973. The comprehensive evaluation data from four regions indicate that the heat source tower heat pump system can achieve different heating and cooling effects in different regions. This indicates that the proposed comprehensive evaluation model for the applicability of heat source tower heat pumps based on this improved method has good evaluation results. It can conduct a good analysis of the applicability of the heat source tower heat pump system, providing effective support for developing reasonable and energy-saving refrigeration and heating methods in different regions.

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Distributed resource allocation optimisation algorithm based on particle swarm optimisation in wireless sensor network

Cited by 3 publications

References 30 publications

TPPSO: A Novel Two-Phase Particle Swarm Optimization

TPPSO: A Novel Two-Phase Particle Swarm Optimization

A Novel Sensor Task Allocation Method Based on Quantum Elite Shuffled Frog Leaping Algorithm in IWSNs

A comprehensive evaluation method of heat source tower heat pump applicability considering regional climate differences

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