Multi-Objective Dispatch Optimization for Microgrids
Microgrid multi-objective dispatch optimization employing an enhanced multi-objective particle swarm algorithm with three objective functions including economic efficiency
Integration of Multi-Agent Learning, Coordination Strategies, and Particle Swarm Optimization
By integrating multi-agent learning, coordination strategies, and particle swarm optimization, we propose a novel distribution network reconfiguration method based on multi-agent particle swarm optimization. This approach leverages the topological structure of particle swarm algorithms to construct a multi-agent system architecture, where each particle functions as an agent that competes and cooperates with neighboring agents. The methodology enables faster and more precise convergence to global optimal solutions. The update rules for particles reduce the generation of infeasible solutions and enhance algorithm efficiency. Experimental results demonstrate that this method achieves high search efficiency and optimization performance. The implementation involves designing agent interactions, defining fitness functions, and optimizing velocity update mechanisms.
Particle Swarm Optimization Toolbox
Particle Swarm Optimization Toolbox - Tested and proven highly effective, featuring intuitive implementation with swarm intelligence algorithms and parameter optimization capabilities for computational research support.
Solving Economic Dispatch Problems Using Particle Swarm Optimization
Implementation of Particle Swarm Optimization (PSO) algorithm to solve economic dispatch problems by determining minimum fuel costs and network losses.
Optimizing RBF Neural Network Weights Using Particle Swarm Algorithm
Enhancing RBF neural networks' nonlinear function approximation capabilities through particle swarm optimization of network weights, with implementation insights into algorithm integration and weight updating mechanisms
Using Particle Swarm Optimization to Train Neural Network Parameters
This package implements Particle Swarm Optimization (PSO) for training neural network parameters. Simply run demoPSOnet.m to observe dynamic 2D visualization of the optimization process, where particle positions represent potential neural network weight configurations and their movement reflects the PSO algorithm's search mechanism through solution space.
Particle Swarm Optimization Algorithm
A basic implementation of particle swarm optimization with reference value for algorithm development
PSO-Optimized Otsu Image Segmentation Algorithm
Implementation of Otsu image segmentation enhanced with Particle Swarm Optimization (PSO) algorithm for optimal threshold selection.
MATLAB Implementation of PSO (Particle Swarm Optimization) Algorithm
MATLAB program for Particle Swarm Optimization (PSO) algorithm demonstrating optimization of a benchmark function. The implementation provides a clear foundation for adapting to other functions with similar methodology. Features intuitive parameter tuning and performance optimization through iterative updates.
Application of Constrained Particle Swarm Optimization in Reactive Power Optimization of Power Systems
Implementation of Constrained Particle Swarm Optimization for Reactive Power Optimization in Power Systems with Algorithmic Enhancements and Code Integration