A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

Delice, Yılmaz; Aydoğan, Emel Kızılkaya; Özcan, Uğur; Ilkay, Mehmet S.

doi:10.1007/s10845-014-0959-7

Cited by 125 publications

(59 citation statements)

References 41 publications

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“…Step 5 According to equations (5), (6), (7), and (8), the inertia factor, the learning factor Laplace coefficient and other variables are updated, and the position of each quantum is updated according to equation (4).…”

Section: Recognition Process Of the Multilinear Multiplier Modelmentioning

confidence: 99%

Adaptive predistortion algorithm based on the multilinear multiplier fusion

Shen

Zhang

2018

Concurrency and Computation

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Summary Aiming at solving the problem of the insufficiency in the predistortion structure and low accuracy of the memory nonlinear power amplifier in the wireless communication system, an adaptive predistortion method for dual loop learning structure based on the fusion of multilinear multiplier model recognition is put forward. On the basis of the real number delay multilinear multiplier model, an improved quantum behaved optimization (QBO) algorithm is adopted in this method to carry out indirect learning structure off‐line training of the multilinear multiplier, so as to determine the model parameters as an initial value of the predistorter. And then, the least square method (LSM) algorithm is used to carry out direct learning structure on‐line micro adjustment of the parameters of the predistorter and to conduct fitting on the nonlinearity and memory effect of the power amplifier. The method has the advantages of simple structure, fast convergence, and high precision, which has avoided the local optimum. The experimental results show that the adjacent channel power in this scheme is improved by approximately 7 dB compared with that of the classical dual loop structure predistortion method, and the linearization performance of the amplifier has been significantly improved, thus its feasibility has been verified.

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Section: Recognition Process Of the Multilinear Multiplier Modelmentioning

confidence: 99%

Adaptive predistortion algorithm based on the multilinear multiplier fusion

Shen

Zhang

2018

Concurrency and Computation

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“…This method simulates the behavior of flying birds and the exchange of information they have to solve their problems (Kennedy & Eberhart, 2001). The PSO method is used for optimization in many areas (Adem et al, 2018;Delice et al, 2017;Collotta et al, 2017). The steps for selecting an attribute with PSO are as follows:…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Diagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization

Adem¹

2018

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Late diagnosis of chronic kidney disease, a disease that has increased in recent years and threatens human life, may lead to dialysis or kidney failure. In this study, kNN, SVM, RBF and Random subspace data mining methods were applied on the data set consisting of 400 samples and 24 attributes taken from UCI for classification of chronic kidney disease with particle swarm optimization (PSO) based feature selection method. As a result of the study, the results of the application of each data mining method are compared with the resultant training and test results. As a result of the comparison, the best performance was found to be 99.75% accuracy with PSO and random subspace method. Moreover, as a method of data mining, it has been seen that the random subspace method has higher accuracy rates than the other methods.

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“…Recently, PSO has been applied to many fields, such as structural optimization (Vagelis et al, 2011), dynamic finite element model updating (Shabbir et al, 2015), vehicle engineering (Battaï a et al, 2013), artificial neural network (Chatterjee et al, 2016;W. Sun et al, 2016) and so on (Amini et al, 2013;Amiri et al, 2012;Delice et al, 2014). Much more studies on PSO can be found in the literature (Eberhart et al, 2001;Ma et al, 2015;Poli et al, 2007;Tyagi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

How to control the crack to propagate along the specified path feasibly?

Cheng

Wang

2018

Computer Methods in Applied Mechanics and Engineering

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A controllable crack propagation (CCP) strategy is suggested. It is well known that crack always leads the failure by crossing the critical domain in engineering structure. Therefore, the CCP method is proposed to control the crack to propagate along the specified path, which is away from the critical domain. To complete this strategy, two optimization methods are engaged. Firstly, a back propagation neural network (BPNN) assisted particle swarm optimization (PSO) is suggested. In this method, to improve the efficiency of CCP, the BPNN is used to build the metamodel instead of the forward evaluation. Secondly, the popular PSO is used. Considering the optimization iteration is a time consuming process, an efficient reanalysis based extended finite element methods (X-FEM) is used to substitute the complete X-FEM solver to calculate the crack propagation path. Moreover, an adaptive subdomain partition strategy is suggested to improve the fitting accuracy between real crack and specified paths. Several typical numerical examples demonstrate that both optimization methods can carry out the CCP. The selection of them should be determined by the tradeoff between efficiency and accuracy.

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A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

Cited by 125 publications

References 41 publications

Adaptive predistortion algorithm based on the multilinear multiplier fusion

Adaptive predistortion algorithm based on the multilinear multiplier fusion

Diagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization

How to control the crack to propagate along the specified path feasibly?

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