Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components
Ali Rıza Yıldız, Pranav Mehta
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.