Particle Swarm Optimization Algorithm
Particle Swarm Optimization effectively enhances system performance and efficiency with robust implementation capabilities
Particle Swarm Optimization Algorithm with Immune Function
Implementation of Particle Swarm Optimization with Immune Function in MATLAB Environment
Multi-Objective Particle Swarm Optimization Algorithm
Multi-Objective Particle Swarm Optimization Algorithm implements the particle swarm optimization principle to simultaneously optimize two objective functions with enhanced convergence mechanisms.
PSO (Particle Swarm Optimization) for Neural Network Optimization with MATLAB Implementation
MATLAB M-file implementation of PSO (Particle Swarm Optimization) algorithm for neural network optimization, featuring complete code structure and parameter configuration
MATLAB Implementation of Genetic Algorithm (GA) with Optimization Algorithm Code Examples
MATLAB implementations of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Algorithm (AS) developed during intelligent computing methods experiments. These codes include GA for Minimum Spanning Tree using Prufer encoding, PSO for unconstrained optimization problems, and AS for Traveling Salesman Problem (TSP). Seeking community improvements and experience exchange to enhance these algorithmic implementations.
Particle Swarm Optimization Algorithm Source Code Collection
Comprehensive source code implementations of various PSO algorithms including Standard PSO, Hybrid PSO, and Improved PSO variants. Features detailed code comments and practical examples, making it an ideal resource for PSO beginners to understand algorithm implementation and application scenarios.
Particle Swarm Optimization for Path Planning
Particle Swarm Optimization (PSO) originated from Complex Adaptive System (CAS) theory, which was formally proposed in 1994. In CAS, members are called agents - for example, in studying bird flock systems, each bird represents an agent. Agents possess adaptability, enabling them to interact with their environment and other agents, while "learning" or "accumulating experience" through these interactions to modify their structure and behavior. The evolution of the entire system includes the emergence of new levels (birth of birds), differentiation and diversity (birds splitting into smaller subgroups), and new themes (discovery of new food sources during foraging). PSO mimics this behavior through mathematical models where particles (agents) iteratively update their positions based on personal and group best solutions.
Optimization Design of PID Controller Using Particle Swarm Algorithm
Optimization Design of PID Controller Based on Particle Swarm Algorithm, Specifically Designed for Beginners' Learning with Code Implementation Insights
MATLAB Implementation of Particle Swarm Optimization Algorithm
MATLAB program implementing Particle Swarm Optimization for multi-objective extremum computation with customizable parameters and visualization capabilities
Particle Swarm Optimization (PSO) Source Code with Implementation Examples
Source code for Particle Swarm Optimization (PSO) algorithm including basic PSO implementation and its applications in function optimization, featuring adaptive weight adjustment and dynamic neighborhood strategies for enhanced performance.