Explore MATLAB source code curated for "优化" with clean implementations, documentation, and examples.
A comprehensive study on reactive power optimization in power systems, implementing a hybrid approach combining Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS) methodologies for enhanced system performance.
Source program implementing RBF neural network optimization through particle swarm algorithm, featuring enhanced learning capabilities and generalization performance with detailed code structure explanations
A straightforward MATLAB implementation using genetic algorithms to optimize SVM parameters, featuring clear code structure with key functions for crossover, mutation, and fitness evaluation - ideal for beginners studying computational intelligence techniques.
Application Background Developed by Sun Chengyi et al. in 1998, the Mind Evolutionary Algorithm (MEA) serves as an effective optimization technique. This chapter details MEA's fundamental concepts and implements the algorithm in MATLAB through a nonlinear function fitting case study. Key Technologies 1. Training/Test Set Generation: Creating datasets using MATLAB's rand() and linspace() functions with proper data partitioning 2. Initial Population Initialization: Implementing population initialization with bounds checking using unifrnd() function 3. Subpopulation Convergence Operation: Performing crossover operations with tournament selection and simulated binary crossover (SBX) 4. Subpopulation Dissimilation Operation: Applying mutation operations using polynomial mutation with adaptive mutation rates 5. Optimal Individual Analysis: Implementing fitness evaluation and elite preservation techniques 6. BP Neural Network Training: Configuring network architecture with newff() and optimizing weights using MEA-based training 7. Simulation Testing and Result Analysis: Conducting performance evaluation with MSE metrics and convergence curve plotting
Implementation of Genetic Algorithm-Optimized BP Neural Networks for Nonlinear System Fitting in MATLAB, combining evolutionary optimization principles with neural network training to enhance predictive performance.
This approach utilizes genetic algorithms to optimize the weights and thresholds of a BP neural network, followed by comprehensive training to develop a predictive model. The process includes practical implementation examples with code-related enhancements.
Implementing PSO algorithm in MATLAB to optimize neuro-fuzzy network controllers, with performance comparison against PID controllers and conventional fuzzy controllers using simulation-based evaluation metrics.
Implementation of particle swarm optimization for BP neural networks in boiler combustion optimization, featuring detailed code explanations, simulation diagrams, and algorithm workflow. The program uses function-based naming convention - simply rename the main file to match the internal function name and execute.
Genetic Algorithms (GAs), proposed in 1962 by Professor Holland at the University of Michigan, are a parallel stochastic search optimization method that simulates natural genetic mechanisms and biological evolution. This approach introduces the biological evolution principle of "survival of the fittest" into encoded parameter populations, where individuals are selected based on fitness functions through genetic operations including selection, crossover, and mutation. High-fitness individuals are preserved while low-fitness individuals are eliminated, creating new populations that inherit previous generation information while demonstrating superior performance. The algorithm iterates until convergence criteria are met, typically involving population initialization, fitness evaluation, and genetic operator application in computational implementations.
Implementation of constrained nonlinear optimization using MATLAB's fmincon function from the Optimization Toolbox, featuring detailed code execution procedures and algorithm explanations.