An improved PSO algorithm based on human approach with levy flight distribution applied to mechanical problems

This study presents a novel enhancement to Technical & Vocational Education & Training based on the Particle Swarm Optimization algorithm (TVETPSO). In this approach, we integrate the Levy Flight strategy in the self-improvement phase rooted in human strategy learning, aimed at improving the optimization capabilities of the algorithm. TVETPSO approach, while effective, often limited exploration of the solution space. By leveraging the Levy flight strategy, characterized by its unique movement patterns of occasional large leaps and frequent smaller steps, our enhancement enables particles to escape local optima and better exploit their experiences. This dynamic exploration facilitates more effective learning and adaptation, leading to improved convergence rates and efficient solutions. In the context of mechanical engineering, this enhancement holds significant promise for optimizing design processes, structural analysis, and resource allocation, ultimately contributing to more efficient and innovative engineering solutions. By fostering a balance between exploration and exploitation, our approach not only advances optimization methodologies but also opens new avenues for applications in complex mechanical systems.