A modified QPSO algorithm applied to engineering inverse problems in electromagnetics

Rehman, Obaid Ur; Yang, Jian; Zhou, Qiang; Yang, Shiyou; Khan, Shafiullah

doi:10.3233/jae-160114

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…Therefore, many researchers have proposed different methodologies for β parameter to control the convergence behavior of the optimizer as stated in [18], [19].…”

Section: Parameter Updating Formulaementioning

confidence: 99%

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Rehman

et al. 2020

IEEE Access

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Quantum inspired particle swarm optimization (QPSO) stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. Although, it has shown good performance in finding the optimal solution to many electromagnetic problems. However, sometimes it falls to local optima when dealing with hard optimization problems. Thus, to preserve a good balance between local and global searches to avoid premature convergence in quantum particle swarm optimization, this paper proposed three enhancements to the original QPSO method, the proposed method is called modified quantum particle swarm optimization (MQPSO) algorithm. Firstly, a novel selection technique is introduced that will choose the best particle among the population within the search domain to achieve a high-performance exploration. Secondly, a new mutation method is used to preserve the easiness of available QPSOs. Also, a dynamic parameter strategy is proposed for further facilitating the algorithm and tradeoff between exploration and exploitation searches. The experimental results obtained by solving standard benchmark functions and an electromagnetic design problem which is the superconducting magnetic energy storage (SMES) system available in both three parameters and eight parameters problems are reported to showcase the usefulness of the proposed approach.

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“…Therefore, many researchers have proposed different methodologies for β parameter to control the convergence behavior of the optimizer as stated in [18], [19].…”

Section: Parameter Updating Formulaementioning

confidence: 99%

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Rehman

et al. 2020

IEEE Access

Self Cite

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“…The quantum-behaved particle swarm optimization algorithm [20] is used to optimize the objective function for obtaining the best atmospheric modified refractivity profile. The classical particle swarm optimization algorithm [21] is a random search algorithm based on swarm intelligence, which has the ability of global approximation, but due to its limited search space, it is easy to fall into the lo-cal extreme value.…”

Section: Quantum-behaved Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

Tang

Cha

Wei³

et al. 2019

Open Geosciences

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This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

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“…In the above formula, i represents the size of the training data. 16 SVM solves the following optimization problem…”

Section: Qpso-svm and Its Optimizationmentioning

confidence: 99%

A novel deformation forecasting method utilizing comprehensive observation data

Yao

2018

Advances in Mechanical Engineering

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Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization-Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization-Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

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A modified QPSO algorithm applied to engineering inverse problems in electromagnetics

Cited by 12 publications

References 26 publications

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

A novel deformation forecasting method utilizing comprehensive observation data

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