Quantum Genetic Algorithm Optimization for Standard Functions
MATLAB source code implementation for optimizing standard functions using quantum genetic algorithms
Neural Network-Based Traffic Flow Prediction in MATLAB
MATLAB Neural Network Traffic Flow Prediction with Optimized Source Code
Artificial Fish Swarm Algorithm (AFSA)
The Artificial Fish Swarm Algorithm (AFSA) is one of the most effective swarm intelligence optimization algorithms, inspired by the collective movement and social behaviors of fish. This algorithm simulates a series of instinctive behaviors where fish naturally maintain their colonies, demonstrating emergent intelligent behavior. Key activities such as foraging, migration, and danger avoidance occur through social interactions within the group, leading to sophisticated collective intelligence. In code implementations, AFSA typically involves simulating fish movement through parameters like visual range, step size, and crowding factor to optimize problem solutions.
Genetic Algorithm Optimization for Simple Functions
Implementing genetic algorithm optimization for simple functions using MATLAB, serving as a practical programming example with code implementation details.
MATLAB Implementation of Particle Swarm Optimization Algorithm
A complete MATLAB implementation of Particle Swarm Optimization (PSO) algorithm featuring swarm intelligence optimization with detailed code comments. This program includes parameter configuration options, fitness function customization, and visualization capabilities for convergence analysis.
Some Differential Evolution Resources
This repository contains source code implementations of Differential Evolution algorithms. These programs feature powerful optimization capabilities suitable for various domains including engineering design, financial modeling, and large-scale network analysis.
BP Neural Network Modeling Optimized by Genetic Algorithm
Modeling nonlinear systems using 1500 datasets for network training and 500 datasets for testing. Since BP neural networks typically initialize weights and thresholds randomly, they often get trapped in local minima. This method employs genetic algorithm optimization for initial weights and thresholds, with comparative analysis of pre- and post-optimization performance. Implementation includes population initialization, fitness function design based on MSE, and chromosome encoding of network parameters.
Multi-Population Chain-Agent Genetic Algorithm
Implementation of multi-population chain-agent genetic algorithm including optimized code and experimental benchmark results. This research-oriented implementation features parallel population evolution with inter-agent communication chains. Please provide feedback if utilized. Contact me for related academic papers on this optimization methodology.
Genetic Algorithm Implementation for 50-Dimensional Function Optimization
Implementation of 50-dimensional function optimization using genetic algorithms, featuring straightforward code structure ideal for beginners. The solution demonstrates key genetic operations including selection, crossover, and mutation with clear variable handling.
n-Player Non-Cooperative Game Nash Equilibrium Computation
Implementation of n-player non-cooperative game Nash equilibrium based on the source code from the paper "An Optimization Formulation to Compute Nash Equilibrium in finite Games" with algorithm analysis and key function descriptions