A nelder mead-infused info algorithm for optimization of mechanical design problems

Abstract

Nature-inspired metaheuristic algorithms have wide applications that have greater emphasis over the classical optimization techniques. The INFO algorithm is developed on the basis of the weighted mean of the vectors, which enhances the superior vector position that enables to get the global optimal solution. Moreover, it evaluates the fitness function within the updating stage, vectors combining, and local search stage. Accordingly, in the present article, a population-based algorithm named weighted mean of vectors (INFO) is hybridized with the Nelder-Mead algorithm (HINFO-NM) and adapted to optimize the standard benchmark function structural optimization of the vehicle suspension arm. This provides a superior convergence rate, prevention of trapping in the local search domain, and class balance between the exploration and exploitation phase. The pursued results suggest that the HINFO-NM algorithm is the robust optimizer that provides the best results compared to the rest of the algorithms. Moreover, the scalability of this algorithm can be realized by having the least standard deviation in the results. The HINFO-NM algorithm can be adopted in a wide range of optimization challenges by assuring superior results obtained in the present article.