Enhanced Swarm Intelligence Algorithm

In this document, we explore an enhanced swarm intelligence algorithm, widely known as the PSO algorithm. PSO is an optimization technique grounded in swarm intelligence principles, designed to address diverse computational problems. The algorithm is available in two distinct implementations: a C language version and a MATLAB version. The C implementation leverages low-level memory management and compiler optimizations, making it suitable for high-performance computing environments requiring maximum execution speed. Conversely, the MATLAB version emphasizes rapid prototyping capabilities through its built-in visualization tools and matrix operation functions, ideal for experimental validation and algorithmic visualization. This paper provides a comprehensive analysis of both PSO variants, examining their respective advantages and limitations through comparative performance metrics. We further discuss practical application scenarios, including parameter tuning methodologies and convergence behavior analysis across different optimization landscapes. Implementation specifics cover key components such as particle velocity updates, neighborhood topologies, and fitness function evaluations in both programming environments.