Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm

Samareh, Hossein; Khoshrou, S.H.; Shahriar, K.; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad H.

doi:10.1016/j.jafrearsci.2017.04.029

Cited by 13 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As opposed to the PSO approach, classical ways in dealing with the nonlinear model have some disadvantages as seen in the previous works [30,31,32,33,34] with required a lot of cumbersome operations like matrix operations, gradient operations, and the Jacobean matrix. In the past [21,22,26,27,28,29,30], researchers estimated the parameters of a large number of various models by using the PSO.…”

Section: The Results For the Estimated Parameters Of The Modelmentioning

confidence: 99%

See 1 more Smart Citation

Medical model estimation with particle swarm optimization

Sarı¹,

Ahmad²,

Uslu³

2021

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

View full text Add to dashboard Cite

In this paper, a nonlinear medical model based on observational variables has been produced and the particle swarm optimization (PSO) technique, which is an e¤ective technique to predict optimum parameters of the biomedical model, has been used. This study has been conducted on a dataset consisting of 539 subjects. For comparison purposes, nonlinear regression analysis, nonlinear deep learning, and nonlinear regression neural network methods are also considered and the PSO results appear to be slightly better than that of other methods. Built on observational variables and …ndings, the model is expected to be a good guide for healthcare professionals in diagnosing pathologies and planning treatment programs for their patients. It is therefore strongly believed that the article will be particularly useful for those interested in emerging biomedical models in various medical modelling areas such as infectious and hematological diseases such as anemia.

show abstract

Section: The Results For the Estimated Parameters Of The Modelmentioning

confidence: 99%

“…Kennedy and Earhart [20] proposed the PSO approach to solve di¤erent problems in the literature [21,22,23,24,25,26,27,28,29], to estimate the parameters of nonlinear models. Besides, di¤erent mathematical method strategies are implemented to predict and optimize problems using the PSO with other methods [30,31,32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Medical model estimation with particle swarm optimization

Sarı¹,

Ahmad²,

Uslu³

2021

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

View full text Add to dashboard Cite

show abstract

“…A hybrid particle swarm optimization and genetic algorithm with population partitioning was utilized for large scale optimization problems [25]. A nonlinear model for predicting the ground vibration was optimized using the combinational particle swarm optimization and genetic algorithm [26]. A two-stage improved genetic algorithm and particle swarm optimization algorithm was proposed to optimize the pressurization scheme of coal bed methane gathering networks [27].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary numerical method for solution of nonlinear differential equations

Mahmoodabadi

2023

Preprint

View full text Add to dashboard Cite

This paper describes a new optimum numerical method to analyze nonlinear quadratic Riccati differential equations. To this end, the Finite Difference Method (FDM) is employed to extract an appropriate discretized objective function, and the penalty method is implemented to convert the constrained problem into an unconstrained one via satisfying the initial conditions. Furthermore, the High Exploration Particle Swarm Optimization (HEPSO) is utilized to find the best numerical values of the nonlinear quadratic Riccati differential equation. In order to illustrate the effectiveness of HEPSO, the optimization trajectories are compared with those of a standard Particle Swarm Optimization (PSO) algorithm. Moreover, comparisons are made between Adomians decomposition method (ADM), Homotopy Perturbation Method (HPM), the exact solution and the proposed method to expose the accuracy, effectiveness and simplicity of the proposed method.

show abstract

“…Finally, the efficiencies of the seven methods were compared. [6] SVM MCD , D Mohamed [10] ANN, FIS MCD , D GHasemi, et al [9] ANFIS B, S, ST , NR, MCD , D Monjezi, et al [7] ANN MCD, D. T C ARmaghani, et al [13] ANN-PSO S, B, ST , PF, Di, NR, RD, SDr HAjihassani et al [15] ANN-ICA BS, ST , PF, C, D, Vp, E Samareh, et al [24] Reg-PSO-GA MCD , D, GSI, σcm , VOD Faradonbeh, et al [25] GP B, S, ST , D,HL, PF, MCD, D T orres, et al [26] MLP MCD , D Agrawal, et al [16] Reg MCD , D Murmu, et al [17] Reg MCD , D, B Nguyen, et al [8] ANN MCD , D, Arthur, et al [18] GPR B,S,PF,D,HL,NB The reminder of the paper is organized as follows. In section 2 blast vibration mechanism and site condition are explained.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines

2019

IJE

View full text Add to dashboard Cite

Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm

Cited by 13 publications

References 29 publications

Medical model estimation with particle swarm optimization

Medical model estimation with particle swarm optimization

An evolutionary numerical method for solution of nonlinear differential equations

Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines

Contact Info

Product

Resources

About