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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Bibliographic Details
Published in:The Journal of supercomputing Vol. 79; no. 13; pp. 14489 - 14544
Main Authors: Braik, Malik, Awadallah, Mohammed, Al-Betar, Mohammed Azmi, Al-Hiary, Heba
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2023
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05215-1