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Summer School: An Introduction to
Meta-heuristics (SSIM'09)
15-17 July 2009
Tehran, Iran
The meeting would be ideal for post-graduate
students involved in optimisation related problems.
The SSIM will take place in Tehran, 15-17 July 2009.
Introduction
In recent decades there has been a growth of interest in
methods for finding optimal solutions to a class of problems called
combinatorial optimisation. Combinatorial problems are normally easy to describe
but difficult to solve. Typical examples of practical combinatorial optimisation
problems are the travelling salesman problem, variants of the assignment
problem, the set covering problem and vehicle routing and scheduling problems.
Due to the practical importance of combinatorial optimisation problems, many
methods have been developed for solving them. These methods can be classified as
either exact or approximate. Exact methods guarantee to find an optimal solution
in a bounded amount of time. Of course, for those combinatorial optimisation
problems which belong to the class NP-hard, exact methods need an exponential
amount of time. These algorithms often require an amount of execution time that
is excessive for practical proposes. Thus, approximation methods are often
considered to be the only practical tools available to solve hard combinatorial
optimisation problems. In this situation, optimality is sacrificed in order to
get good quality solutions in a reasonable amount of time.
A new kind of approximation methods has emerged that is organised around an
interaction between local improvement procedures and high-level strategies which
aim to explore the search space in a way that is capable of escaping from an
unsatisfactory local optimum. These methods are nowadays commonly called metaheuristics.
A Metaheuristic can be viewed as a general algorithmic framework, which can be
applied to wide set of different optimisation problems with relatively few
modifications being necessary to adapt them to a specific problem.
We will outline several popular metaheuristics for combinatorial optimisation
problems, including simulated annealing (SA), tabu search (TS), genetic
algorithms (GA), greedy randomised adaptive search procedures (GRASP), and ant
colony optimisation (ACO). Subsequently, a recently developed Fuzzy Greedy
Search (FGS) heuristic will be discussed.
Contact Details
For further information and registration, please contact:
Dr. Kaveh Sheibani, Chair
Tadbir Institute for Operational Research,
Systems Design and Financial Services Ltd.
Tehran 19797-13673 Iran
Tel +98 (0)21 2271 7097
Fax +98 (0)21 2271 6816
Email tadbir@tadbirstm.org.ir
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