Cuckoo Search Algorithm and Particle Swarm Optimization (PSO) Algorithm
Cuckoo Search Algorithm and PSO Algorithm with Code Implementation Details
PSO-Based PID Parameter Optimization
Optimizing PID Parameters Using Particle Swarm Optimization Algorithm
Enhanced Swarm Intelligence Algorithm
Enhanced Swarm Intelligence Algorithm, commonly referred to as PSO (Particle Swarm Optimization), featuring dual implementations in C and MATLAB
Particle Swarm Optimization (PSO) Algorithm for Enhanced K-means Clustering
MATLAB implementation of Particle Swarm Optimization algorithm applied to optimize K-means clustering. Successfully tested on MATLAB 6.5/7.1 versions (other versions not verified). The code integrates PSO's global search capabilities with K-means clustering to achieve improved clustering accuracy and performance.
Research on Microgrid Capacity Optimization Configuration Based on PSO Algorithm
Complete MATLAB implementation for microgrid capacity optimization using Particle Swarm Optimization algorithm with full simulation capabilities and configuration analysis
Particle Swarm Optimization (PSO) - An Evolutionary Computation Technique
Particle Swarm Optimization (PSO) is an evolutionary computation technique invented by Dr. Eberhart and Dr. Kennedy, inspired by bird flock predatory behavior. Similar to genetic algorithms, PSO is an iterative optimization tool that initializes with random solutions and searches for optimal values through iterations. Unlike genetic algorithms, PSO eliminates crossover and mutation operations, instead having particles follow the optimal particle in solution space. Future chapters will detail implementation steps and comparisons with genetic algorithms. PSO's advantages include simplicity of implementation with minimal parameter tuning, currently applied extensively in function optimization and neural network training. Code implementations typically involve velocity updates and position adjustments using personal and global best values.
Particle Swarm Optimization (PSO) Algorithm Implementation
A complete PSO algorithm process executing n-loop iterations, returning minimum/maximum average fitness values, online performance, and offline performance metrics. The implementation handles particle initialization, velocity/position updates, and fitness evaluation through modular function design.
Standard PSO Implementation in MATLAB with Algorithm Documentation
This program provides a standard Particle Swarm Optimization (PSO) implementation in MATLAB, accompanied by a PDF document explaining the PSO algorithm and its code implementation details.
Economic Dispatch for 40 Generating Units Using Particle Swarm Optimization Algorithm
Implementation of PSO algorithm for economic dispatch in a 40-unit generation system incorporating wind power integration
Optimization of PID Parameters Using Particle Swarm Algorithm
Implementation of PID parameter optimization through PSO algorithm with published research demonstrating excellent performance and robust code implementation