MATLAB Implementation of Particle Swarm Optimization Algorithm
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
Particle Swarm Optimization (PSO) is a population-based intelligent optimization algorithm that simulates cooperative behaviors observed in bird flocks or fish schools. The algorithm continuously adjusts particle positions and velocities to search for optimal solutions through iterative updates. Key implementation aspects include velocity calculation using cognitive and social components, position updating with boundary constraints handling, and fitness evaluation for solution quality assessment. PSO finds widespread applications across various domains including engineering design, economic optimization, and data mining. This article provides MATLAB code examples demonstrating core PSO components such as initialization procedures, main optimization loops, and convergence monitoring techniques, offering practical insights for algorithm implementation and customization.
- Login to Download
- 1 Credits