QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distribution

Bhatia, Amandeep Singh; Saggi, Mandeep Kaur; Zheng, Shenggen

doi:10.1007/s11128-020-02842-y

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…Shanshan Tu et al [42] proposed updating of crossover parameter to improve the quantum PSO performance and global search abilities. An approach proposed in [43] combines QPSO with Cauchy mutation operator (QPSO-CD) which adds extended capabilities for global hunt.…”

Section: Related Workmentioning

confidence: 99%

“…We have also compared performance of EDCQPSO with PSO and its versions. These algorithms are; PSO [15], PDQPSO [40], QPSO -CD [42], CLQPSO [43], and CSQPSO [44]. All these algorithms are simulated in the same environment on the IEEE CEC2019 benchmark functions by setting parameters the same as that of the original paper.…”

Section: B Comparisons Of the Edcqpso With Other Versions Of Psomentioning

confidence: 99%

“…Details of parameters chosen for every algorithm are presented in Table 6. QPSO-CD [43] α ∈ [1.0, 0.5], = 2, 1 = 2 = 2.…”

Section: B Comparisons Of the Edcqpso With Other Versions Of Psomentioning

confidence: 99%

“…In the second step, it computes the location of the normal sensor node. For performance analysis, the results of EDCQPSO are compared with PDQPSO [40], CLQPSO [42], QPSO-CD [43] and CSQPSO [44]. With random deployment of sensor nodes in localization area, average localization error (ALE) is calculated as a standard statistical metric and given by…”

Section: Edcqpso For Iot Applicationsmentioning

confidence: 99%

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Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

et al. 2021

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An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

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Section: Related Workmentioning

confidence: 99%

Section: B Comparisons Of the Edcqpso With Other Versions Of Psomentioning

confidence: 99%

“…Details of parameters chosen for every algorithm are presented in Table 6. QPSO-CD [43] α ∈ [1.0, 0.5], = 2, 1 = 2 = 2.…”

Section: B Comparisons Of the Edcqpso With Other Versions Of Psomentioning

confidence: 99%

Section: Edcqpso For Iot Applicationsmentioning

confidence: 99%

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Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

et al. 2021

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“…For the purpose of organizing intelligent computing as an optimum structure in this research, the MFNN includes one convolutional block and a recurrent neural net (RNN), fuzzy units, and RNNs. In order to automatically define the optimal architecture of an MFFN that demands global multi-dimensional optimization and definition of conditions of optimal MFFN architecture, we improved modified PSO [5] based on a quantum-behaved PSO algorithm with Laplace distribution (QPSO-LD) [11] and the proposed hierarchical encoder of the dimension component of particles. We tuned the MFNN based on a modified multidimension quantum-behaved PSO algorithm with Laplace distribution (MD QPSO-LD).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reconfigurable Photovoltaic System

et al. 2021

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The global maximum power point tracking of a PV array under partial shading represents a global optimization problem. Conventional maximum power point tracking algorithms fail to track the global maximum power point, and global optimization algorithms do not provide global maximum power point in real-time mode due to a slow convergence process. This paper presents an intelligent reconfigurable photovoltaic system on the basis of a modified fuzzy neural net that includes a convolutional block, recurrent networks, and fuzzy units. We tune the modified fuzzy neural net based on modified multi-dimension particle swarm optimization. Based on the processing of the sensors’ signals and the photovoltaic array’s image, the tuned modified fuzzy neural net generates an electrical interconnection matrix of a photovoltaic total-cross-tied array, which reaches the global maximum power point under non-homogeneous insolation. Thus, the intelligent reconfigurable photovoltaic system represents an effective machine learning application in a photovoltaic system. We demonstrate the advantages of the created intelligent reconfigurable photovoltaic system by simulations. The simulation results reveal robustness against photovoltaic system uncertainties and better performance and control speed of the proposed intelligent reconfigurable photovoltaic system under non-homogeneous insolation as compared to a GA-based reconfiguration total-cross-tied photovoltaic system.

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Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy

Zhou

Shu-hua

et al. 2022

Quantum Inf Process

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QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distribution

Cited by 14 publications

References 39 publications

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

Intelligent Reconfigurable Photovoltaic System

Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy

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