Optimisation of Biochemical Systems Production using Hybrid of Newton Method, Differential Evolution Algorithm and Cooperative Coevolution Algorithm

Ismail, Mohd Arfian; Mezhuyev, Vitaliy; Moorthy, Kohbalan; Kasim, Shahreen; Ibrahim, Ashraf Osman

doi:10.11591/ijeecs.v8.i1.pp27-35

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…The comparison table also shows that using optimization algorithm(PSO) to classify the intrusion is sometimes gives a better result than some classifiers like (SVM). It is, therefore, suggested that there should be a balance between the need for accuracy and the need for decreasing the detection time in the future studies by referring to various other works available such as [34]- [40].…”

Section: Discussionmentioning

confidence: 99%

Review on Intrusion Detection System Based on The Goal of The Detection System

Khaleel

Ismail

Yunan³

et al. 2018

IJIE

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Section: Discussionmentioning

confidence: 99%

Review on Intrusion Detection System Based on The Goal of The Detection System

Khaleel

Ismail

Yunan³

et al. 2018

IJIE

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“…Step 1: Initialize the first iteration (t) of particles p(t). Each particle is generated randomly using Equation 12. Each particle represents the variables in NES.…”

Section: E Combination Of Newton Methods and Particle Swarm Optimizationmentioning

confidence: 99%

“…Numerous works have been conducted in the optimization of biochemical systems [3], [4], [10]. All of them modeled the biochemical systems in NES and solved NES using optimization methods such as genetic algorithm [5], [11], differential evolution algorithm [12], [13], linear programming method [2], [14], [15], and geometric programming method [3], [16]. However, the results produced by current works are low and can be improved [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization

Ismail¹,

Mezhuyev²,

Darmawan³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the presented paper, an improved method that combines the Newton method with Particle Swarm Optimization (PSO) algorithm to optimize the production of biochemical systems was discussed and presented in detail. The optimization of the biochemical system's production became difficult and complicated when it involves a large size of biochemical systems that have many components and interaction between chemical. Also, two objectives and several constraints make the optimization process difficult. To overcome these situations, the proposed method was proposed by treating the biochemical systems as a nonlinear equations system and then optimizes using PSO. The proposed method was proposed to improve the biochemical system's production and at the same time reduce the total of chemical concentration involves. In the proposed method, the Newton method was used to deal with nonlinear equations system, while the PSO algorithm was utilized to fine-tune the variables in nonlinear equations system. The main reason for using the Newton method is its simplicity in solving the nonlinear equations system. The justification of choosing PSO algorithm is its direct implementation and effectiveness in the optimization process. In order to evaluate the proposed method, two biochemical systems were used, which were E.coli pathway and S. cerevisiae pathway. The experimental results showed that the proposed method was able to achieve the best result as compared to other works. Keywords-newton method; particle swarm optimization; optimization; biochemical systems; computational intelligence.

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“…Inadvisable parameter settings result in inferior classification performance. For the future work, this study can be extend into two part; firstly by improving the performance of GA such as hybrid GA with other method as works done by [22][23][24], and secondly by apply feature selection method in SVM for optimal parameter setting as proposed in [25].…”

Section: Conclusion and Recommendationmentioning

confidence: 99%

A new model for iris data set classification based on linear support vector machine parameter's optimization

Hussain¹,

Ibraheem²,

Alsajri³

et al. 2020

IJECE

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Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

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Optimisation of Biochemical Systems Production using Hybrid of Newton Method, Differential Evolution Algorithm and Cooperative Coevolution Algorithm

Cited by 11 publications

References 21 publications

Review on Intrusion Detection System Based on The Goal of The Detection System

Review on Intrusion Detection System Based on The Goal of The Detection System

Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization

A new model for iris data set classification based on linear support vector machine parameter's optimization

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