| Description
| A metaheuristic is an algorithmic process that guides several subordinate heuristics by combining intelligently different concepts for exploring and exploiting the search space. Metaheuristics have many successful applications in industrial and scientific worlds. The metaheuristics course covers the following topics.
1 Trajectory methods, including
(1) Simulated annealing
(2) Tabu search
(3) Variable neighborhood search
(4) Iterated local search
2 Population Based Methods
(1) Ant colony algorithm
(2) Evolutionary algorithm
3 Hybridization of metaheuristics
(1) Cooperative search
(2) Component exchange
(3) Integration of metaheuristics and exact algorithms
4 Applications of metaheuristics
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