Bio-inspired Algorithms for Optimization in Mechanical Design Systems

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i12.1

Prof (Dr) Ajay Shriram Kushwaha

Knowledge Park III, Greater Noida, U.P. 201310, India

kushwaha.ajay22@gmail.com

Abstract

Bio-inspired algorithms have emerged as powerful optimization techniques, offering robust, adaptive, and scalable solutions for complex engineering design problems. This manuscript investigates the application of four representative bio-inspired algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—to optimization challenges in mechanical design systems up to the year 2022. We present a detailed comparison of their performance on benchmark design tasks such as truss sizing, cam profile optimization, and mechanism parameter tuning. Statistical analysis over multiple independent runs evaluates convergence speed, solution quality, and robustness. The methodology section outlines the common framework, parameter settings, and evaluation metrics employed. Results indicate that while GA and PSO achieve high-quality solutions, ABC demonstrates superior robustness under noisy objective functions, and ACO excels in discrete combinatorial settings. The discussion interprets these findings in the context of mechanical design requirements, emphasizing trade‑offs between exploration and exploitation. Finally, we identify limitations and propose future research directions, including hybrid algorithm development, multi‑objective extensions, and real‑time optimization for adaptive manufacturing systems.

Keywords

Bio-inspired algorithms; mechanical design optimization; genetic algorithm; particle swarm optimization; ant colony optimization; artificial bee colony

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