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A unified-metaheuristic framework

Osman, Ibrahim H. (1999) A unified-metaheuristic framework. In: Imam, Ibrahim and Kodratoff, Yves and El-Dessouki, Ayman and Ali, Moonis, eds. Multiple Approaches to Intelligent Systems 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. Lecture Notes in Artificial Intelligence . Springer, Berlin, Germany, pp. 11-12. ISBN 978-3-540-66076-7. E-ISBN 978-3-540-48765-4. (doi:10.1007/978-3-540-48765-4_3) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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http://dx.doi.org/10.1007/978-3-540-48765-4_3

Abstract

In recent years, there have been significant advances in the theory and application of metaheuristics to approximate solutions of complex optimization Problems. A metaheuristic is an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high quality solutions, [6] [8]. It may manipulate a complete (or incomplete) Single Solution or a collection of solutions at each iteration. The subordinate heuristics may be high (or low) level procedures, or a simple local search, or just a construction method. The family of metaheuristics includes, but is not limited to, Adaptive memory programming, Ants Systems, Evolutionary methods, Genetic algorithms, Greedy randomised adaptive search procedures, Neural networks, Simulated annealing, Scatter search, Tabu search and their hybrids.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-540-48765-4_3
Uncontrolled keywords: Operational Research, Tabu Search, Scatter Search, High Quality Solution, Complex Optimization Problem
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: F.D. Zabet
Date Deposited: 06 Mar 1914 13:03 UTC
Last Modified: 31 May 2019 08:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16420 (The current URI for this page, for reference purposes)
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