This talk will present a tutorial on the implementation and use of metaheuristics and approximation algorithms for solving hard combinatorial optimization problems. There will be introduced the basics of the most studied algorithms for finding good suboptimal solutions, including genetic algorithms, simulated annealing, tabu search, variable neighborhood search, greedy randomized adaptive search procedures (GRASP), path relinking, and scatter search. After introducing the basics of these algorithms, implementation issues will be discussed, illustrating the ease in which sequential and parallel heuristics can be developed.

A short introduction to metaheuristics and approximation algorithms for solving hard combinatorial optimization problems

FESTA, PAOLA
2011

Abstract

This talk will present a tutorial on the implementation and use of metaheuristics and approximation algorithms for solving hard combinatorial optimization problems. There will be introduced the basics of the most studied algorithms for finding good suboptimal solutions, including genetic algorithms, simulated annealing, tabu search, variable neighborhood search, greedy randomized adaptive search procedures (GRASP), path relinking, and scatter search. After introducing the basics of these algorithms, implementation issues will be discussed, illustrating the ease in which sequential and parallel heuristics can be developed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/395169
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