Particle Swarm

Particle swarm optimization (PSO) is a metaheuristic algorithm inspired by the social behavior of bird flocking or fish schooling, used to find optimal solutions in complex search spaces. Current research focuses on improving PSO's efficiency and effectiveness through modifications like spherical vector-based PSO and incorporating it into hybrid approaches with other algorithms (e.g., combining PSO with convolutional neural networks or using it for initialization in resampling strategies). These advancements are driving applications across diverse fields, including robotics (trajectory optimization and energy reduction), unmanned aerial vehicle path planning, and analog circuit design optimization, demonstrating PSO's versatility and impact on solving real-world problems.