Exploring optimization algorithms for challenging multidimensional optimization problems: A comparative approach.

This paper investigates the Nelder-Mead, Powell, and Differential Evolution algorithms in the optimization of a multidimensional optimization problem. Actually, this type of the problem is difficult to solve since the objective function mixes quadratic terms and trigonometric functions. Optimization techniques were use; the ideal values for the design variables that minimize the objective function i discovered. Using 3D surface plots, contour plots, convergence rate plots, and fitness landscape plots, the optimization process made visible. The outcomes show how each algorithm works and behaves in terms of convergence, giving information about both how well it performs and how the objective function is distributed. The results add to our understanding of optimization strategies and offer suggestions for choosing the best algorithms for situations with similar complexity in optimization. Finally, the numerical optimization approach has been implemented by Python language. [ABSTRACT FROM AUTHOR]