Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger-Reference-Cited by-同舟云学术

Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger

Published:2022-09-01 Issue:9 Volume:64 Page:1325-1331
ISSN:0025-5300
Container-title:Materials Testing
language:en
Short-container-title:

Author:

Gürses Dildar¹,Mehta Pranav²,Patel Vivek³,Sait Sadiq M.⁴,Yildiz Ali Riza¹

Affiliation:

1. Department of Mechanical Engineering , Bursa Uludag Universitesi, Uludağ University , Görükle bursa , Bursa 16059 , Turkey

2. Department of Mechanical Engineering , Dharmsinh Desai University , Nadiad , Gujarat 387001 , India

3. Department of Mechanical Engineering , Pandit Deendayal Petroleum University , Gandhinagar , Gujarat , India

4. King Fahd University of Petroleum & Minerals , Dhahran , Saudi Arabia

Abstract

Abstract Adaptability of the metaheuristic (MH) algorithms in multidisciplinary platforms confirms its significance and effectiveness for the solution of the constraints problems. In this article, one of the imperative thermal system components-plate fin heat exchangers is economically optimized using the novel artificial gorilla troops optimization algorithms (AGTOAs). The cost optimization challenge of the PFHE includes the initial and running cost that needs to be minimized by optimizing several design variables subjecting to critical boundary conditions. To confirm the performance of the AGTOA, the statistical results obtained were compared with nine benchmark MHs algorithms. It was found that AGTO is a robust optimization algorithm because it was able to fetch the best results for the function with 100% of the success rate compared to the rest of the algorithms. Moreover, considering the superior results obtained from the AGTO, it can be applied to numerous applications of the engineering design optimization.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://www.degruyter.com/document/doi/10.1515/mt-2022-0049/pdf

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