Explore MATLAB source code curated for "优化算法" with clean implementations, documentation, and examples.
Comprehensive collection of standard benchmark functions including Sphere, Rosenbrock, Griewank, Ackley, Rastrigin and other classical test functions, suitable for various intelligent optimization algorithms such as Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization and other classical optimization techniques
Application Background This code is based on the book "Practical Computer Simulation for Chemical Engineering," which includes MATLAB programming tutorials and calculations for various chemical processes such as distillation columns, reactors, control systems, differential equations, and algebraic equations. The book also provides examples of chemical process optimization. It is recommended to purchase the book and study it alongside this code for effective learning, as the code is specifically written to correspond with the book's examples. Key Technologies The code implements optimization algorithms including quadratic programming and least squares methods. It utilizes MATLAB's Optimization Toolbox to minimize objective functions, covering design optimization, operational optimization, and global optimization. The code also includes parameter estimation and model identification components, such as kinetic parameter estimation and heat transfer parameter calculation.
A novel optimization algorithm with MATLAB implementation, featuring swarm intelligence principles and iterative solution enhancement techniques for learning and application.
Optimizes neural network performance by using genetic algorithms to determine superior initial weights and thresholds, which enhances convergence and solution quality.
An optimized algorithm for minimum connected dominating set using minimum spanning tree approach
Brain Storm Optimization Algorithm - An Emerging Approach in Swarm Intelligence Algorithms
OFDM carrier frequency offset estimation optimization algorithm code, originally from an outstanding 2007 master's thesis with high reference value, featuring algorithm implementation with key MATLAB functions for frequency offset correction.
Implementation Tutorial for Genetic Algorithm-Optimized BP Neural Network Algorithm For detailed explanations with code implementation examples, please refer to the included tutorial. Due to file size limitations, contact me for high-definition tutorials with complete MATLAB/Python code demonstrations.
Solving transportation problems using optimization algorithms. 1. Compares different solution methods without using linprog, 2. Uses Northwest Corner Method to obtain initial basic feasible solution, applies Stepping-Stone Method to determine exiting basis variables and update transportation matrix, 3. Note: This approach cannot handle unbalanced transportation problems
Particle Swarm Optimization algorithms have been implemented and tested. Test functions should be added to fitness.m for evaluation, enabling performance analysis through customizable benchmark functions.