Lagrangian Relaxation Method for Unit Commitment Problem Solving
Implementing Lagrangian Relaxation method to solve unit commitment problems with consideration of domain search randomness and heuristic optimization techniques
Depth-First Search Algorithm MATLAB Source Code with Randomness Enhancement
MATLAB implementation of depth-first search algorithm with added randomness - This program originates from international sources and represents a standard DFS implementation capable of node traversal and cycle detection. The original algorithm has been enhanced with random branching selection, creating a randomized depth-first search variant. Please refer to the original English comments for comprehensive details about the base implementation.
Traffic Flow Prediction Using Genetic Algorithm-Optimized Wavelet Neural Networks (GA-WNN)
Urban traffic flow exhibits high complexity, time-varying characteristics, and randomness. Real-time accurate traffic flow prediction is crucial for intelligent transportation systems, particularly in advanced traffic management and traveler information systems. This paper presents a GA-WNN prediction model that combines genetic algorithms with wavelet neural networks. The genetic algorithm performs preliminary optimization of connection weights and scaling/translation parameters, overcoming limitations of gradient descent methods like local minima and oscillation effects. Simulation experiments validate GA-WNN's effectiveness for short-term traffic flow prediction.