Open Access

ARTICLE

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

Sumika Chauhan¹, Govind Vashishtha^1,*, Riya Singh², Divesh Bharti³

1 Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na-Grobli 15, Wroclaw, 50-421, Poland
2 Department of Mechanical Engineering, GLA University, Mathura, 281406, India
3 Precision Metrology Laboratory, Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Punjab, 148106, India

* Corresponding Authors: Govind Vashishtha. Email: ,

Computer Modeling in Engineering & Sciences 2025, 144(1), 309-343. https://doi.org/10.32604/cmes.2025.068628

Received 02 June 2025; Accepted 02 July 2025; Issue published 31 July 2025

Abstract

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.

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Keywords

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