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All Journal Journal of Fundamental Mathematics and Applications (JFMA) Indonesian Journal of Applied Mathematics
Alridha, Ahmed Hasan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Physics

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OPTIMIZATION ALGORITHMS FOR PROJECTILE MOTION: MAXIMIZING RANGE AND DETERMINING OPTIMAL LAUNCH ANGLE Alridha, Ahmed Hasan
Journal of Fundamental Mathematics and Applications (JFMA) Vol 6, No 2 (2023)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v6i2.20750

Abstract

In this paper, we undertake an in-depth exploration of the optimization of parameters governing the trajectory of a projectile. Our primary objective is the determination of the optimal launch angle and initial velocity that yield the maximum achievable range for the projectile. To accomplish this, we leverage five distinct optimization methodologies, specifically the Nelder-Mead, Powell, L-BFGS-B, TNC, and SLSQP algorithms, in pursuit of our research goals. This paper offers a comprehensive analysis of the optimization procedures, shedding light on the impact of these diverse algorithms on the resultant outcomes. For each set of optimized parameters, the manuscript conducts extensive simulations of the projectile’s trajectory, presenting visual depictions of the paths traversed by the projectile. Additionally, our study incorporates comparative charts to emphasize the performance distinctions among various algorithms with respect to both maximum range and launch angle.
Efficiency and Accuracy in Quadratic Curve Fitting: A Comparative Analysis of Optimization Techniques: A Comparative Analysis of Optimization Techniques Alridha, Ahmed Hasan
Indonesian Journal of Applied Mathematics Vol. 3 No. 2 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 2 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v3i2.1575

Abstract

In this paper, we investigate an optimization methods might be applied for solving curve fitting by making use of a quadratic model. To discover the ideal parameters for the quadratic model, synthetic experimental data is generated, and then two unique optimization approaches, namely differential evolution and the Nelder-Mead algorithm, are applied to the problem in order to find the optimal values for those parameters. The mean squared error as well as the correlation coefficient are both metrics that are incorporated into the objective function. When the results of these algorithms are compared, trade-offs between the rate of convergence and the quality of the fit are revealed. This work sheds light on the necessity of selecting proper optimization algorithms for specific circumstances and provides insights into the balance that must be struck between accurate curve fitting and efficient use of computational resources in the process of curve fitting.
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