Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification

Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial windin...

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Veröffentlicht in:	The Journal of supercomputing Jg. 79; H. 13; S. 14489 - 14544
Hauptverfasser:	Braik, Malik, Awadallah, Mohammed, Al-Betar, Mohammed Azmi, Al-Hiary, Heba
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Classification Compilers Complex systems Computer Science Convergence Datasets Genetic algorithms Heuristic Identification methods Interpreters Mathematical models Modelling Multilayer perceptrons Network management systems Nonlinear systems Optimization Optimization algorithms Optimization techniques Parameter identification Processor Architectures Programming Languages Subsystems System theory Whales & whaling Winding Industrial winding process Whale optimization algorithm Artificial neural network Multilayer Perceptron Classification
ISSN:	0920-8542, 1573-0484
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Abstract	Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales’ movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems.
AbstractList	Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales’ movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems.
Author	Al-Betar, Mohammed Azmi Braik, Malik Awadallah, Mohammed Al-Hiary, Heba
Author_xml	– sequence: 1 givenname: Malik surname: Braik fullname: Braik, Malik email: mbraik@bau.edu.jo organization: Department of Computer Science, Al-Balqa Applied University – sequence: 2 givenname: Mohammed surname: Awadallah fullname: Awadallah, Mohammed organization: Department of Computer Science, Al-Aqsa University, Artificial Intelligence Research Center (AIRC), Ajman University – sequence: 3 givenname: Mohammed Azmi surname: Al-Betar fullname: Al-Betar, Mohammed Azmi organization: Artificial Intelligence Research Center (AIRC), Ajman University, Department of Information Technology, Al-Balqa Applied University – sequence: 4 givenname: Heba surname: Al-Hiary fullname: Al-Hiary, Heba organization: Department of Computer Information Systems, Al-Balqa Applied University
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Keywords	Industrial winding process Whale optimization algorithm Artificial neural network Multilayer Perceptron Classification
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SubjectTerms	Algorithms Artificial neural networks Classification Compilers Complex systems Computer Science Convergence Datasets Genetic algorithms Heuristic Identification methods Interpreters Mathematical models Modelling Multilayer perceptrons Network management systems Nonlinear systems Optimization Optimization algorithms Optimization techniques Parameter identification Processor Architectures Programming Languages Subsystems System theory Whales & whaling Winding
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Title	Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification
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