Chapter

Adaptive and Multilevel Metaheuristics

Volume 136 of the series Studies in Computational Intelligence pp 3-29

Hyperheuristics: Recent Developments

  • Konstantin ChakhlevitchAffiliated withCASS Business School, City University
  • , Peter CowlingAffiliated withDepartment of Computing, University of Bradford

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Abstract

Given their economic importance, there is continuing research interest in providing better and better solutions to real-world scheduling problems. The models for such problems are increasingly complex and exhaustive search for optimal solutions is usually impractical. Moreover, difficulty in accurately modelling the problems means that mathematically “optimal” solutions may not actually be the best possible solutions in practice. Therefore heuristic methods are often used, which do not guarantee optimal or even near optimal solutions. The main goal of heuristics is to produce solutions of acceptable quality in reasonable time. The problem owners often prefer simple, easy to implement heuristic approaches which do not require significant amount of resources for their development and implementation [12]. However, such individual heuristics do not always perform well for the variety of problem instances which may be encountered in practice. There is a wide range of modern heuristics known from the literature which are specifically designed and tuned to solve certain classes of optimisation problems. These methods are based on the partial search of the solution space and often referred as metaheuristics.

Keywords

Hyperheuristics multilevel heuristics greedy heuristics learning