A Novel Approach Based on Recuperated Seed Search Optimization for Solving Mechanical Engineering Design Problems

This paper introduces a novel optimization approach called Recuperated Seed Search Optimization (RSSO), designed to address challenges in solving mechanical engineering design problems. Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex, nonlinear natures. The Sperm Swarm Optimization (SSO) algorithm, which mimics the sperm’s movement to reach an egg, is one such technique. To improve SSO, researchers combined it with three strategies: opposition-based learning (OBL), Cauchy mutation (CM), and position clamping. OBL introduces diversity to SSO by exploring opposite solutions, speeding up convergence. CM enhances both exploration and exploitation capabilities throughout the optimization process. This combined approach, RSSO, has been rigorously tested on standard benchmark functions, real-world engineering problems, and through statistical analysis (Wilcoxon test). The results demonstrate that RSSO significantly outperforms other optimization algorithms, achieving faster convergence and better solutions. The paper details the RSSO algorithm, discusses its implementation, and presents comparative results that validate its effectiveness in solving complex engineering design challenges.