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A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications

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Abstract

This review article provides a comprehensive analysis of the optimization techniques used in a wide range of engineering applications. The comparison of various approaches such as Response surface methodology (RSM), Genetic algorithm (GA) and Artificial neural network (ANN) towards optimization problems is widely elaborated. The factors that affect the optimization using various techniques are addressed along with the safety precautions to be followed in a sequential manner to achieve a better optimization model. Furthermore, the coupling of two distinct algorithms (RSM-GA, ANN-GA) are explained and this hybrid approach provides a better localizing of the optimal point with a higher accuracy.

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Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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  1. Department of Energy and Environment, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

    Anto Susaimanickam & Premalatha Manickam

  2. Department of High Voltage Engineering, University of Applied Sciences, 02763, Zittau, Germany

    Amalanathan Arputhasamy Joseph

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AS-Conceptualization, methodology, writing original draft; PM-Review and Supervision; AAJ-Conceptualization, methodology, writing original draft. All the authors have read and agreed to the published version of the manuscript.

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Susaimanickam, A., Manickam, P. & Joseph, A.A. A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications. Arch Computat Methods Eng 30, 4831–4853 (2023). https://doi.org/10.1007/s11831-023-09963-4

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