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Metaheuristic Optimization Techniques with Matlab


This course is your introduction to the world of optimization strategies. If you already know Matlab or pythons and wish to discover ways to discover the very best answer for any drawback then this course is for you.

On this course, we’ll begin with an introduction to the world of optimization then we’ll take the Breadth First Search and Depth First Search that are crucial for path planning. Then we’ll dive into Simulated Annealing, the genetic algorithm, Particle Swarm Optimization and Ant Colony Optimization. After understanding all of them we’ll see how one can use them on Matlab. That is all executed with quizzes after every two lectures to check your understanding for every subject and on the finish of the course you’ll get an examination to check how a lot you understood the course and whether or not you should use these optimization strategies to unravel any drawback.

The course is essential for anybody who desires to study optimization and how one can discover the very best answer for any drawback. Optimization is essential for some synthetic intelligence purposes and in autonomous programs so this course will open numerous doorways for you sooner or later. The course is split into 10 lectures

1) Introduction the place you’ll get to know extra about optimization and its differing types

2) Deterministic strategies that are BFS and DFS

3) SA

4) GA

5) PSO

6) ACO

7) Matlab for SA

8) Matlab for GA

9) Matlab for ACO

10) Matlab for PSO

11) ultimate Examination of 25 questions



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