PSO with dynamic acceleration coefficient based on mutiple constraint satisfaction: Implementing Fuzzy Inference System

Banerjee, Chayan; Sawal, Ruchi

doi:10.1109/icaecc.2014.7002381

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…In the second subsection, the IACPSO algorithm is proposed in view of the fact that the population diversity of the SPSO algorithm decreases in the late iterations, and the SPSO algorithm is prone to fall into local optimization and premature convergence [20][21][22][23]. The innovation of the IACPSO algorithm is to propose a novel strategy for adaptively adjusting the inertia weights and learning factors according to the degree of population prematureness and the fitness values of the particles [24].…”

Section: Proposal Of the Iacpso Algorithmmentioning

confidence: 99%

“…As a result, the number of array elements is reduced, while the complexity of the feeding circuit is also reduced. In addition, to optimize the layout of the SRPRA, an improved adaptive chaotic particle swarm optimization (IACPSO) algorithm is proposed in this paper [20][21][22][23][24]. To address the shortcoming that the SPSO algorithm may be trapped in a local optimal solution, we propose a new strategy of adaptive inertia weights and learning factors to improve the global traversal and global search.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Synthesis Method of a Sparse Rectangular Planar Receiving Array for Microwave Power Transmission

Li¹,

Liu²

2023

PIER C

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A novel synthesis method of a sparse rectangular planar receiving array (SRPRA) to maximize the power transmission efficiency (PTE ) for microwave power transmission (MPT) is proposed in this paper. The array element positions of the SRPRA are symmetrically distributed among different quadrants such that the array elements at symmetrical positions receive the same power, and the SRPRA adopts a sparse layout. This reduces the number of array elements and simplifies the complexity of the feeding network. An improved adaptive chaotic particle swarm optimization (IACPSO) algorithm is proposed for the optimization synthesis problem of the SRPRA. Through the optimization of the proposed IACPSO algorithm, the optimal element layout of the SRPRA can be obtained efficiently to get the maximum PTE. In addition, we conduct a series of simulation experiments to verify the advantages of the proposed SRPRA model and the effectiveness of the IACPSO algorithm. Firstly, we analyze the effects of different parameters on the synthesis results of the SRPRA. Secondly, comparing the results with those of the sparse random circular aperture array (SRCAA), it is demonstrated that the SRPRA synthesized with the IACPSO algorithm can obtain higher PTE with fewer elements and has a relatively simple feeding network. Finally, compared with the standard particle swarm optimization (SPSO) algorithm, the proposed IACPSO algorithm can effectively and stably obtain the synthesis results of the SRPRA under different parameters. Therefore, the SRPRA is suitable for creating an efficient MPT system.

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Section: Proposal Of the Iacpso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Synthesis Method of a Sparse Rectangular Planar Receiving Array for Microwave Power Transmission

Li¹,

Liu²

2023

PIER C

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show abstract

“…We use the DCFPSO algorithm [20,21] to find the optimal element distribution, X, and the corresponding optimal element weight, w opt [w opt 1 , w opt 2 , ..., w opt n , ..., w opt N ] H . In the process of optimization, the optimal element distribution is constrained by the minimum element distance, d min , and array apertures, L x × L y .…”

Section: Snandpa Synthesis Of Transmitting Arraymentioning

confidence: 99%

“…In this paper, a sparse array synthesis method considering both array performance and system cost is proposed. The proposed method first applies the dual compression factor particle swarm optimization (DCFPSO) [20,21] to improve sparse array performance by optimizing BCE, and then simplifies the feed network with a subarray partition technique. In consequence, the optimized synthesis model of sparse nonuniform-amplitude nonuniform-distribution planar array (SNANDPA) is obtained.…”

Section: Introductionmentioning

confidence: 99%

Novel Sparse Planar Array Synthesis Model for Microwave Power Transmission Systems With High Efficiency and Low Cost

Li¹,

Chang²

2021

PIER C

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A novel algorithm is developed to realize the optimal synthesis of a sparse nonuniformamplitude nonuniform-distribution planar array (SNANDPA) in microwave power transmission (MPT) systems. The dual compression factor particle swarm optimization (DCFPSO) algorithm and the subarray partition technique are adopted to realize the optimal synthesis of SNANDPA. The DCFPSO algorithm first optimizes beam collection efficiency (BCE ) and side-lobe level outside the receiving region (CSL) of SNANDPA which ensure efficient energy transmission of an MPT system and suppress the influence of electromagnetic wave radiated by antenna array on the environment. The subarray partition technique then simplifies the feed network to minimize the system cost. SNANDPA parameters including transmitting aperture, receiving aperture, BCE, CSL, power pattern, element weight, and element distribution, can be obtained efficiently via the proposed method. Representative numerical cases under the different numbers of subarray and elements conditions are analyzed and compared with those of other two traditional MPT array models. Experimental results show that, when the transmitting aperture is 4.5λ × 4.5λ and the square receiving region u 0 = v 0 = 0.2, BCE and CSL are 94.96% and −17.09 dB, respectively, and only 64 elements and 8 amplifiers are required. We conclude that the proposed model can be used to create an efficient and low-cost MPT system.

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“…On the other hand, various approaches to improve the search skills and performance of metaheuristics are adapted to their processing phases. The approaches such as dynamic population [6], weighted accelerations [7], chaotic mapping [8] are the preferred methods. One of the places where performance improvement is made is the initialization phase.…”

Section: Introductionmentioning

confidence: 99%

Opposite Based Crow Search Algorithm for Solving Optimization Problems

Durmuş

2021

International Scientific and Vocational Studies Journal

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This article proposes an opposite based learning (OBL) enhanced crow search algorithm (CSA) version for solving optimization problems. The proposed method, named the opposite based CSA (ObCSA), starts searching with individuals with higher fitness in the initial phase of the evolutionary process. In this way, it is aimed to improve the convergence performance of the basic CSA. To validate the proposed method, a set of benchmark test suit of different of features is chosen. Its convergence characteristic and statistical results are compared with the basic CSA. The results obtained show that the proposed method improves the convergence performance of the basic CSA. And the statistical results indicate that it manages to reach the near optimal solution and increases the quality of the solution.

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PSO with dynamic acceleration coefficient based on mutiple constraint satisfaction: Implementing Fuzzy Inference System

Cited by 5 publications

References 8 publications

A Novel Synthesis Method of a Sparse Rectangular Planar Receiving Array for Microwave Power Transmission

A Novel Synthesis Method of a Sparse Rectangular Planar Receiving Array for Microwave Power Transmission

Novel Sparse Planar Array Synthesis Model for Microwave Power Transmission Systems With High Efficiency and Low Cost

Opposite Based Crow Search Algorithm for Solving Optimization Problems

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