Solving Constrained Optimization Problems Using the Lagrange Multiplier Method
Practical implementation example demonstrating the use of Lagrange Multiplier Method to solve constrained optimization problems with code-based explanations.
Gauss-Newton Method for Solving Unconstrained Optimization Problems
Gauss-Newton Method: A Fundamental Algorithm for Unconstrained Optimization with Nonlinear Least-Squares Applications
Optimization Problem Solver with Boundary Constraints
John D Errico developed and enhanced an optimization solver that efficiently handles variables within specified boundary constraints, implementing robust algorithms for constrained optimization problems.
Solving Power System Optimization Problems Using Standard Genetic Algorithm
Application of standard genetic algorithm to solve large-scale power system optimization problems with 40-node network configuration
Implementation of Ant Colony Optimization Algorithm
This approach applies the Ant Colony Optimization algorithm to solve constrained optimization problems, extending the foundational algorithm with constraint-handling mechanisms through pheromone matrix modifications and penalty function integration.
Solving Power System Optimization Problems Using Standard PSO Algorithm
Implementation of standard Particle Swarm Optimization (PSO) algorithm for large-scale power system optimization with 40-node problem configuration
Cuckoo Search Algorithm MATLAB Implementation with Code Descriptions
Application Background: Cuckoo Search (CS) algorithm, also known as Cuckoo Optimization, is an emerging metaheuristic algorithm proposed by Professor Xin-She Yang from Cambridge University and S. Deb in 2009. This algorithm effectively solves optimization problems by simulating the brood parasitic behavior of certain cuckoo species, combined with Levy flight search mechanisms. Research demonstrates that CS outperforms many other swarm optimization algorithms. Key Technical Aspects: This MATLAB implementation simulates cuckoo nesting behavior through three idealized rules with position updates using Levy flights. The code includes parameter configuration for population size, discovery probability, and step size control, providing a practical framework for solving engineering optimization and machine learning problems.
QPSO Algorithm for Power System Optimization Problems
Implementation of QPSO Algorithm for Large-Scale Power System Optimization with 40-Node Case Study