Comprehensive Collection of All Enhanced PSO Algorithms

This collection encompasses all enhanced versions of Particle Swarm Optimization (PSO) algorithms, designed to provide substantial assistance and guidance for optimization challenges. Each modified algorithm includes: - Detailed explanations of underlying principles and mathematical formulations - Specific application domains with real-world use cases - Ready-to-use code implementations featuring key parameters like inertia weight adjustment strategies and neighborhood topologies - Performance comparisons highlighting convergence behavior and solution quality The algorithms incorporate advanced techniques such as: 1. Adaptive inertia control using linear/non-linear decreasing functions 2. Hybrid approaches combining PSO with genetic algorithm operators 3. Multi-swarm architectures with dynamic population management 4. Constraint-handling mechanisms for constrained optimization problems We provide comprehensive documentation covering initialization procedures, velocity update mechanisms, and position boundary handling to ensure proper implementation. Each algorithm module contains configurable parameters (swarm size, cognitive/social factors, maximum iterations) and includes visualization tools for tracking optimization progress. These resources aim to fulfill diverse requirements in optimization research while providing extensive knowledge and practical resources for PSO-based problem solving.