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A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data

A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data

Harish Garg
Copyright: © 2015 |Pages: 35
ISBN13: 9781466672581|ISBN10: 1466672587|EISBN13: 9781466672598
DOI: 10.4018/978-1-4666-7258-1.ch020
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MLA

Garg, Harish. "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data." Handbook of Research on Artificial Intelligence Techniques and Algorithms, edited by Pandian Vasant, IGI Global, 2015, pp. 620-654. https://doi.org/10.4018/978-1-4666-7258-1.ch020

APA

Garg, H. (2015). A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data. In P. Vasant (Ed.), Handbook of Research on Artificial Intelligence Techniques and Algorithms (pp. 620-654). IGI Global. https://doi.org/10.4018/978-1-4666-7258-1.ch020

Chicago

Garg, Harish. "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data." In Handbook of Research on Artificial Intelligence Techniques and Algorithms, edited by Pandian Vasant, 620-654. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7258-1.ch020

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Abstract

The main objective of this chapter is to present a novel hybrid GA-GSA algorithm to permit the reliability analyst to increase the performance of the system by utilizing the uncertain data. Since the analysis based on the collected data mostly contains a lot of uncertainties, the corresponding results obtained do not tell the exact nature of the system. Therefore, to handle this issue, the proposed algorithm maximizes the Reliability, Availability, and Maintainability (RAM) parameters simultaneously for increasing the performance and productivity of the system. The conflicts between the objectives are resolved with the help of intuitionistic fuzzy set theory. The optimal design parameters corresponding to each component of the system are evaluated by solving a nonlinear optimization problem and compared their results with other methods. The stability of these optimal parameters is justified by means of pooled t-test statistics. Based on these optimal design parameters, an investigation has been done for finding the most critical component of the system for saving money, manpower, and time, as well as increasing the performance of the system. Finally, to illustrate the methodology, a numerical example is studied.

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