A General Framework for Constructive Meta-heuristics

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Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.
Original languageEnglish
Title of host publicationOperations Research/Management Science at Work
EditorsErhan Kozan, Azuma Ohuchi
Place of PublicationBoston
Number of pages18
ISBN (Electronic)978-1-4615-0819-9
ISBN (Print)978-1-4613-5254-9
Publication statusPublished - 2002


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