Metaheuristic Optimization Techniques with Matlab

Explore graph search methods by comparing breadth-first search and depth-first search. See how queue (FIFO) and stack (LIFO) drive exploration and how BFS can yield optimal, complete solutions.

Learn how simulated annealing solves optimization problems by using random solutions, probabilistic acceptance, and temperature cooling to balance exploration and exploitation, illustrated with the traveling salesman problem.

Explore genetic algorithms as population-based optimization methods inspired by evolution; learn chromosome representations, selection strategies such as roulette wheel and tournament, crossover, mutation, and elitism to solve complex problems.

Explore particle swarm optimization, where particles update velocity and position using personal and global best to find optima. Learn inertia weight, acceleration coefficients, random factors, and synchronous and asynchronous updates.

Explore how ant colony optimization, with Matlab, solves permutation and binary problems by using pheromone trails, transition rules, and evaporation to discover shortest paths and optimal item selections.

Apply simulated annealing in Matlab to solve the traveling salesman problem by initializing a distance matrix and path, swapping cities under a cooling schedule and probabilistic acceptance.

Learn how to implement a MATLAB genetic algorithm for the traveling salesman problem, covering population setup, elite selection, Davis crossover, mutation, and cost-based selection to improve solutions.

This Matlab lecture applies ant colony optimization to the traveling salesman problem, detailing transition rules, probability calculations, roulette wheel selection, and evaporation to converge on optimal solutions.

demonstrates implementing particle swarm optimization in Matlab to solve complex optimization problems, updating velocity and position using personal best and global best, with objective functions and constraints.