Simulated Annealing Particle Swarm Optimization Algorithm
Simulated Annealing Particle Swarm Optimization - A practical and efficient implementation with comprehensive code structure and parameter tuning guidelines
Local Model of Particle Swarm Optimization Algorithm
Implementation of PSO's local model demonstrates superior performance compared to global model variants, featuring more reliable convergence to optimal solutions despite slower convergence rates, with key implementations including neighborhood topology management and local best position tracking.
Particle Swarm Optimization Algorithm for Finding Shortest Paths in Obstacle-Embedded Path Graphs
MATLAB implementation of Particle Swarm Optimization algorithm for identifying shortest paths in obstacle-containing path graphs, featuring obstacle-aware path planning and convergence optimization techniques.
Personally Collected Various Intelligent Algorithms with Source Code
My personal collection of intelligent algorithms includes over 20 source code implementations covering: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), hybrid algorithms like Genetic-Neural Network, PSO-SVM, and PSO-Neural Network, each featuring distinct optimization strategies and parameter tuning approaches.
PSO-Optimized LSSVM Algorithm
Application Background: This program implements the Particle Swarm Optimization (PSO) algorithm to optimize Least Squares Support Vector Machine (LSSVM) parameters. Key Technologies: PSO heuristic optimization, LSSVM machine learning, hyperparameter tuning, classification and regression tasks.
Multi-Objective Particle Swarm Optimization with Pareto Dominance
MATLAB-implemented multi-objective particle swarm optimization program based on Pareto dominance theory, validated through multiple benchmark test functions with excellent performance results.
Simulated Annealing-Particle Swarm Optimization Hybrid Algorithm
Simulated Annealing-Particle Swarm Optimization Algorithm - This program combines simulated annealing with particle swarm optimization to achieve superior parameter optimization results through enhanced global search capabilities and convergence performance
Dual-Fitness Particle Swarm Optimization Algorithm
A MATLAB-implemented dual-fitness particle swarm optimization algorithm demonstrating superior convergence performance, particularly effective for solving optimal power flow and other power system optimization problems. The implementation features dynamic velocity updating and position adjustment mechanisms with dual fitness evaluation for enhanced search capability.
A Globally Convergent Particle Swarm Optimization Algorithm with Significantly Improved Convergence Speed
An enhanced particle swarm optimization algorithm featuring guaranteed global convergence and substantially accelerated convergence rates through novel search strategies and adaptive parameter mechanisms
Griewank Function Implementation in MATLAB
MATLAB implementation of the Griewank function for evaluating particle swarm optimization algorithm fitness with detailed code structure explanation