MATLAB Program Collection for Simulated Annealing, Tabu Search, Genetic Algorithms, and Neural Networks

In this article, we explore four powerful optimization algorithms—Simulated Annealing, Tabu Search, Genetic Algorithms, and Neural Networks—along with their practical implementations in MATLAB. These algorithms serve as essential tools for solving complex computational problems. Simulated Annealing is a probabilistic optimization method inspired by statistical mechanics, designed to find global optima in large solution spaces by gradually reducing a temperature parameter to control acceptance probabilities of worse solutions. MATLAB implementations typically include temperature scheduling functions and energy calculation modules. Tabu Search employs neighborhood search strategies with memory structures (tabu lists) to prevent cycling and escape local optima, where key MATLAB components manage candidate moves and aspiration criteria. Genetic Algorithms simulate natural evolution through selection, crossover, and mutation operations, with MATLAB code featuring population initialization, fitness evaluation, and genetic operator functions. Neural Networks, biologically-inspired computational models, handle tasks like classification, regression, and clustering using MATLAB's built-in functions for layer configuration, activation functions, and backpropagation training. These algorithms are widely applied in MATLAB for image processing, data mining, and machine learning projects, providing customizable frameworks through script-based implementations or Neural Network Toolbox integration.