Standard Particle Swarm Optimization Algorithm

In computer science, the Standard Particle Swarm Optimization (SPSO) algorithm is an optimization technique inspired by the flocking behavior of birds. The core objective of this algorithm is to locate optimal solutions within a search space by tracking each particle's position and velocity. At each iteration, particles update their positions based on their personal best-known positions and the global best position discovered by the entire swarm. This algorithm employs key mathematical operations including velocity updates using inertia weights and acceleration coefficients, typically implemented through vector addition and multiplication. The basic pseudocode structure involves initializing particle positions randomly, evaluating fitness functions, updating personal and global bests, and adjusting velocities until convergence criteria are met. SPSO has been widely applied across various domains including engineering design, machine learning, image processing, and financial analysis. Recent research has introduced several enhanced variants such as Adaptive Particle Swarm Optimization (which dynamically adjusts parameters) and Chaotic Particle Swarm Optimization (incorporating chaos theory for improved exploration). Typical implementations involve configuring swarm size, iteration limits, and boundary constraints while maintaining balance between exploration and exploitation phases.