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The course aims to teach approximation methods and metaheuristic algorithms to solve combinatorial problems. The methods include basic search procedures, simulated annealing, tabu search, genetic algorithms ant colony algorithms and their hybrids. Ultimate goal is to teach techniques for designing a heuristic algorithm for an engineering problem, evaluatingits performance and computational complexity.
Introduction and categorization of heuristics. Overview of basic heuristic search procedures. Metaheuristics. Simulated annealing. Tabu search. Genetic algorithms. Ant colony algorithms. Evaluation of heuristic performance. Computational complexity of heuristics.
Upon succesful completion of this course, a student will be able to
1. Comprehend the basic types of heuristic methods(B2, a2)
2. Apply fundamental concepts of heuristics in solving various optimization problems [B3] [a2]
3. Use metaheuristics including simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. [B3] [a2]
4. Compare the quality of different heuristic approaches (B6, b2,c)
5. Solve an engineering problem by using an appropriate heuristic method . [e, k] [B3]
6. Analyse the results of a heuristic method for an engineering problem [b2,] [B4]