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.
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Osman, I.H. (1999). A Unified-Metaheuristic Framework. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_3
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DOI: https://doi.org/10.1007/978-3-540-48765-4_3
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