Tabu search with multi-level neighborhood structures for high dimensional problems

Metaheuristics have been successfully applied to solve different types of numerical and combinatorial optimization problems. However, they often lose their effectiveness and advantages when applied to large and complex problems. Moreover, the contributions of metaheuristics that deal with high dimen...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2012-09, Vol.37 (2), p.189-206
Main Authors: Hedar, Abdel-Rahman, Ali, Ahmed Fouad
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Combinatorial analysis
Computation
Computational efficiency
Computer Science
Genetic algorithms
Heuristic
Machines
Manufacturing
Mathematical analysis
Mathematical functions
Mathematical models
Mechanical Engineering
Methods
Optimization
Partitioning
Performance evaluation
Processes
Tabu search
Variables
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Summary:Metaheuristics have been successfully applied to solve different types of numerical and combinatorial optimization problems. However, they often lose their effectiveness and advantages when applied to large and complex problems. Moreover, the contributions of metaheuristics that deal with high dimensional problems are still very limited compared with low and middle dimensional problems. In this paper, Tabu Search algorithm based on variable partitioning is proposed for solving high dimensional problems. Specifically, multi-level neighborhood structures are constructed by partitioning the variables into small groups. Some of these groups are selected and the neighborhood of their variables are explored. The computational results shown later indicate that exploring the neighborhood of all variables at the same time, even for structured neighborhood, can badly effect the progress of the search. However, exploring the neighborhood gradually through smaller number of variables can give better results. The variable partitioning mechanism used in the proposed method can allow the search process to explore the region around the current iterate solution more precisely. Actually, this partitioning mechanism works as dimensional reduction mechanism. For high dimensional problems, extensive computational studies are carried out to evaluate the performance of newly proposed algorithm on large number of benchmark functions. The results show that the proposed method is promising and produces high quality solutions within low computational costs.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-011-0321-0