Chaotic Random Cloud Drift Optimization with Kent Initialization and Opposition-Based Learning for Engineering Design Problems

To overcome challenges associated with Cloud Drift Optimization (CDO), primarily involving early stagnation and the imbalance between global exploration and local exploitation in tackling high-dimensional, complex optimization tasks, an improved variant named Chaotic Random Cloud Drift Optimization (CRCDO) is proposed in this study. First, a Kent chaotic initialization mapping is adopted to generate diverse initial solutions. Second, the parameter sensitivity is adjusted to appropriately strengthen the local exploitation capability of the algorithm. Third, a centroid opposition-based learning strategy is introduced to generate mutated clouds, which enables the algorithm to escape local optima. Owing to the cooperative effect of these three strategies, CRCDO achieves faster convergence and higher computational accuracy while maintaining dynamic stability. To evaluate its performance, CRCDO is compared with eleven well-established optimization algorithms on the CEC2017 suites. The experimental results demonstrate that CRCDO exhibits superior optimization performance on a wide range of complex problems. Furthermore, the proposed algorithm has been successfully applied to three representative engineering design problems, including robot gripper design, welded beam design, and compression spring design, as well as a regression task for predicting praeruptorin A and praeruptorin B in Peucedanum praeruptorum. The results show that CRCDO achieves faster convergence, higher solution accuracy, and greater stability on the benchmark problems, while its engineering and prediction applications demonstrate robustness and practical applicability across different problem classes.