2- Course Aim :

The course covers a range of advanced meta-heuristics such as Local Search, Genetic Algorithms, Tabu Search, Simulated Annealing, Variable Neighborhood Search, Multi-Start methods, GRASP algorithms, Scatter Search, Ant Colony Systems, Particle Swarm Optimization, etc. The main objective of this course is studying techniques for solving optimization problems by using meta-heuristics to design efficient and flexible algorithms Course covers theoretical design as well as implementation issues in the main application areas: Transport problems, Bioinformatics, Data Mining, Network Design, Scheduling, Routing, Location, Packing, and other Combinatorial Optimization.