Evaluation the Performance of Sine Cosine Algorithm in Solving Pressure Vessel Engineering Design Problem

Ghulam Ali Sabery; Ghulam Hassan Danishyar; Mohammad Arman Osmani

doi:10.55544/jrasb.3.3.8

Authors

Ghulam Ali Sabery Assistant Professor, Department of Mathematics & Physics, Faculty of Engineering, Balkh University, Mazar-e-Sharif, Balkh, AFGHANISTAN.
Ghulam Hassan Danishyar Assistant Professor, Department of Mathematics & Physics, Faculty of Engineering, Balkh University, Mazar-e-Sharif, Balkh, AFGHANISTAN.
Mohammad Arman Osmani Assistant Professor, Department of Civil and Industrial Engineering, Faculty of Engineering, Balkh University, Mazar-e-Sharif, Balkh, AFGHANISTAN.

DOI:

https://doi.org/10.55544/jrasb.3.3.8

Keywords:

Metaheuristic Algorithm, Optimization, Pressure Vessel Design, Search Agent Success Rate, Sine Cosine Algorithm

Abstract

The Sine Cosine Algorithm (SCA) is one of the population-based metaheuristic optimization algorithms inspired by the oscillation and convergence properties of sine and cosine functions. The SCA smoothly transits from exploration to exploitation using adaptive range change in the sine and cosine functions. On the other hand, pressure vessel design is a complex engineering structural optimization problem, which aims to find the best possible design for a vessel that can withstand high pressure. This typically involves optimizing the material, shape, and thickness of the vessel to minimize welding, the material, and forming cost while ensuring it meets safety and performance requirements. This paper evaluates the performance of SCA for solving pressure vessel design problems. The result produced by SCA is compared with the results obtained by other well-known metaheuristic optimization algorithms, namely; ABC, ACO, BBO, CMA-ES, CS, DE, GA, GSA, GWO, HSA, PSO, SSO, TLBO and TSA. The experimental results demonstrated that SCA provides a competitive solution to other metaheuristic optimization algorithms with the advantage of having a simple structured search equation. Moreover, the performance of SCA is checked by different numbers of populations and the results indicated that the best possible population size should be 30 and 40. In addition to this, the SCA search agent success rate is checked for different numbers of populations and results show that the search agent success rate do not exceed 4.2%.

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