Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components

Abstract

Abstract The adaptability of metaheuristics is proliferating rapidly for optimizing engineering designs and structures. The imperative need for the fuel-efficient design of vehicles with lightweight structures is also a soaring demand raised by the different industries. This research contributes to both areas by using both the hybrid Taguchi salp swarm algorithm-Nelder–Mead (HTSSA-NM) and the manta ray foraging optimization (MRFO) algorithm to optimize the structure and shape of the automobile brake pedal. The results of HTSSA-NM and MRFO are compared with some well-established metaheuristics such as horse herd optimization algorithm, black widow optimization algorithm, squirrel search algorithm, and Harris Hawks optimization algorithm to verify its performance. It is observed that HTSSA-NM is robust and superior in terms of optimizing shape with the least mass of the engineering structures. Also, HTSSA-NM realize the best value for the present problem compared to the rest of the optimizer.