Linear Decreasing Particle Swarm Optimization Algorithm

This article explores the Particle Swarm Optimization (PSO) algorithm and its applications. PSO is an intelligence-based optimization algorithm inspired by collective behaviors observed in nature, such as bird flocks and fish schools. The algorithm simulates particles' movement and interaction within the solution space to locate optimal solutions. In practical implementations, PSO has been widely applied in function optimization, fuzzy control, neural networks, and other domains. This paper focuses specifically on the linear decreasing weight PSO variant, which employs a linearly decreasing inertia weight strategy to balance global exploration and local exploitation capabilities. Implementation typically involves initializing particle positions and velocities, then iteratively updating them using velocity and position update equations while linearly reducing the inertia weight from a maximum to minimum value over iterations. The optimization performance and application scenarios of this algorithm will be detailed in subsequent sections.