Optimal design of automobile seat components using chaotic enzyme action optimization algorithm

Abstract

An enhanced version of the recently released enzyme action optimizer (EAO), the chaotic enzyme action optimizer (CEAO), is presented in this paper. It was created to solve challenging constrained engineering optimization problems. The algorithm improves exploration-exploitation balance, convergence speed, and robustness by incorporating chaotic maps like sine, cosine, and logistic functions. Five real-world mechanical design problems were used to thoroughly validate CEAO's performance. CEAO outperformed POA and PKO in rolling element bearing design, achieving the best objective value. It confirmed accuracy and stability by minimizing the weight for Belleville spring optimization, with an exceptionally low standard deviation. CEAO outperformed other competing algorithms in the multiple disc clutch brake problem, yielding the lowest fitness value. It outperformed nine cutting-edge metaheuristics and produced the best result in the cost minimization of shell-and-tube heat exchangers. Lastly, CEAO outperformed manual and algorithmic counterparts for an automotive bracket design by reducing component weight under a maximum stress constraint. The superiority of CEAO in terms of convergence, stability, and solution quality was confirmed. These results show that CEAO is a robust and highly competitive metaheuristic for resolving optimization problems at the industrial scale.