Improving production quality of a hot-rolling industrial process via genetic programming model

Sheta, Alaa; Faris, Hossam; Öznergiz, Ertan

doi:10.1504/ijcat.2014.062360

Cited by 13 publications

(3 citation statements)

References 46 publications

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“…The algorithm evolves these genes through genetic operations such as mutation, crossover, and selection, seeking to optimize the fitness function, which measures how well the set of genes fits a given dataset. MG-GP has been known to be a powerful tool for solving complex regression problems, such as those found in modeling and optimization of manufacturing processes [36], [37], software effort estimation [38], image reconstruction [39], and many others [40], [41]. It can address problems with datasets that have a complex and noisy relationship.…”

Section: What Is Multigene Symbolic Regression Gp?mentioning

confidence: 99%

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

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The earth's population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.

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Section: What Is Multigene Symbolic Regression Gp?mentioning

confidence: 99%

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

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“…In the past two decades, the literature of metaheuristic search has expanded extensively. Some of the well-known metaheuristic approaches are Genetic Algorithms [17], Genetic Programming [18][19][20], Particle Swarm Optimization [21][22], Simulated Annealing [23], Artificial Bee Colony (ABC) [24], Cuckoo Search [25][26], etc.…”

Section: Introductionmentioning

confidence: 99%

Resolution of economic dispatch problem of the morocco network using crow search algorithm

Habachi

Touil

Boulal

et al. 2019

IJEECS

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The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes use of Crow Search Algorithm (CSA), for solving the economic dispatch of two electricity networks: a testing system 6 units and the morocco network the crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows..The results obtained by CSA are compared with various results obtained in the literature. Simulation results shows that using CSA can lead to finding stable and adequate power generated that can fulfill the need of both the civil and industrial areas.

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“…The development of well-founded models to simulate the outputs of complex industrial processes is substantial enough to produce a good approximation of the output parameters. When compared to empirical methods that modelled the same process, model-based methods performed significantly better (Öznergiz et al, 2009;Sheta et al, 2009Sheta et al, , 2013. This expansion has shifted the focus of the intensification from control to modelling.…”

mentioning

confidence: 99%

Enhanced meta‐heuristic methods for industrial winding process modelling

Albashish,

Mustafa,

Khurma

et al. 2023

Expert Systems

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Nonlinear industrial system modelling entails two critical phases: The first is selecting a method in order to estimate the parameter list values, and the second is selecting a proper model structure with a relatively short parameter list. Developing a comprehensive model for an industrial design process is critical for the model‐based control system. This article presents a model‐based strategy that aims to develop three linear and three nonlinear dynamic models using three well‐known meta‐heuristic optimization algorithms to simulate a challenging plant‐wide process. As a case study, an industrial real winding process (WP) is targeted to accomplish the aim of this study. The algorithms have been optimized to find the best weights of the inputs of the WP with a key issue to effectively describe the behaviour aspects of the process. To test the validity of the developed models, a series of experiments were carried out on each of the developed linear and nonlinear models. Several relevant evaluation metric measures are used to demonstrate the models' performance level. The experimental results for training and test sets of 1250 independent samples for each set based upon the proposed modelling schemes show that the mean square error to correctly model the WP occurred in less than 0.001. A comparison of the developed intelligent linear and nonlinear models with the Auto‐Regressive Integrated Moving Average (ARIMA) and Multiple Linear Regression (MLR) models obtained through the evaluation criteria asserts the effectiveness of the proposed models‐based approaches.

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Improving production quality of a hot-rolling industrial process via genetic programming model

Cited by 13 publications

References 46 publications

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Resolution of economic dispatch problem of the morocco network using crow search algorithm

Enhanced meta‐heuristic methods for industrial winding process modelling

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