AIMS AND OBJECTIVES OF THE COURSE: The aim of this course is to introduce students to the general theory of Metaheuristic and the features in the design of parallel implementations for solving specific classes of combinatorial problems. At the end of the course the students are expected to know and be able to apply the concepts, taxonomy, principles, specifics and possibilities for practical implementation of metaheuristic algorithms for development of various applications that require solving hard optimization problems.
DESCRIPTION OF THE COURSE: The main topics concern: Metaheuristics concepts; Taxonomy; Concepts of evolutionary computations; Genetic algorithms; Meta-genetic algorithms; Simulated annealing; Algorithm Metropolis; Local search using memory structures; Tabu-search; Variable Neighborhood Search (VNS); Local search; Iterative local search; Greedy Randomized Adaptive Search Procedure (GRASP); Ant Colony Optimization; Memetic algorithms. Upon completion of the course students will know the concepts, principles, models and paradigms of metaheuristic and design of the software for their implementation; be able to do a comparative analysis and assess the advantages and disadvantages between alternative solutions; be able to create effective program implementation, profiling, assessment and analysis of the performance of metaheuristic algorithms.
