Chaotic status-based algorithm for optimization of industrial components

Abstract To improve exploration capacity, convergence stability, and global search efficiency, this study suggests a Chaotic Status-Based Algorithm (CSBA) by incorporating several chaotic maps, such as sine, tent, cosine, logistic, Chebyshev, and Gauss, hybridized with the Status-Based Optimizer. Two real-world engineering design problems, such as robotic gripper arm optimization and automotive suspension lower control arm optimization, are used to assess the suggested CSBA. Compared with traditional metaheuristics, CSBA exhibited better convergence behavior for the robotic gripper, efficiently minimizing the difference between the maximum and minimum gripping forces across seven design variables and nine constraints. For CSBA, statistical performance metrics verified reduced variance, enhanced robustness, and quicker convergence. These findings show that the status-based framework’s optimization performance is greatly improved by the proposed chaotic augmentation, making CSBA a dependable and competitive tool for lightweight, economical engineering design optimization.