FRIME Optimization Algorithm for Engineering Applications

To address the limitations of the RIME optimization algorithm—specifically its susceptibility to local optima and insufficient late-stage exploration and exploitation capabilities—this paper proposes a multi-strategy enhanced RIME optimization algorithm (FRIME). The proposed enhancements include a population initialization strategy (TLCERL) that integrates Tent-Logistic-Cosine mapping and elite reverse learning to augment population diversity, the incorporation of the Jaya algorithm during early iterations to strengthen global search capability, and the implementation of a Smooth Exploration System in later iterations to mitigate premature convergence. The performance of the FRIME algorithm was evaluated using the CEC2022 benchmark functions, demonstrating superior solution accuracy and stability compared to the original RIME and other state-of-the-art algorithms. In the two engineering optimization issues, the convergence accuracy and speed of the FRIME algorithm both surpass those of all the compared algorithms, thereby verifying the practicality of the algorithm in practical engineering applications.