Global Optimization

Clemens Heitzinger ()
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Clemens Heitzinger: Technische Universität Wien, Center for Artificial Intelligence and Machine Learning (CAIML)and Department of Mathematics and Geoinformation

Chapter Chapter 11 in Algorithms with JULIA, 2022, pp 307-328 from Springer

Abstract: Abstract The optimization of functions is a topic of both great theoretical and practical importance. Optimization problems occur in many different contexts, where a common setting is that a model of the quantity of interest is to be optimized with respect to its parameters. In this chapter, we present important classes of algorithms for global optimization, i.e., for finding all global minima or maxima of a given function on a given domain disregarding any local optima. The methods described here are simulated annealing, particle-swarm optimization, and genetic algorithms. A list of benchmark problems of varying difficulty is also included, inviting the reader to experiment with the optimization algorithms and their parameters. Local optimization methods, usually based on the gradient of the function, are discussed in the next chapter.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-16560-3_11

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DOI: 10.1007/978-3-031-16560-3_11

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