Stochastic Search in Metaheuristics

Walter J. Gutjahr ()
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Walter J. Gutjahr: University of Vienna

Chapter Chapter 19 in Handbook of Metaheuristics, 2010, pp 573-597 from Springer

Abstract: Abstract Stochastic search is a key mechanism underlying many metaheuristics. The chapter starts with the presentation of a general framework algorithm in the form of a stochastic search process that contains a large variety of familiar metaheuristic techniques as special cases. Based on this unified view, questions concerning convergence and runtime are discussed on the level of a theoretical analysis. Concrete examples from diverse metaheuristic fields are given. In connection with runtime results, important topics as instance difficulty, phase transitions, parameter choice, No-Free-Lunch theorems, or fitness landscape analysis are addressed. Furthermore, a short sketch of the theory of black-box optimization is given, and generalizations of results to stochastic search under noise are outlined.

Keywords: Particle Swarm Optimization; Simulated Annealing; Variable Neighborhood Search; Fitness Landscape; Metaheuristic Algorithm (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_19

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