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New Optimization Techniques in Engineering pp 167–217Cite as

SOMA — Self-Organizing Migrating Algorithm

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 141))

Abstract

In recent years, a broad class of algorithms has been developed for stochastic optimization, i.e. for optimizing systems where the functional relationship between the independent input variables x and output (objective function) y of a system S is not known. Using stochastic optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA) and Differential Evolution (DE), a system is confronted with a random input vector and its response is measured. This response is then used by the algorithm to tune the input vector in such a way that the system produces the desired output or target value in an iterative process.

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Authors
  1. Ivan Zelinka
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© 2004 Springer-Verlag Berlin Heidelberg

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Zelinka, I. (2004). SOMA — Self-Organizing Migrating Algorithm. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39930-8_7

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  • DOI: https://doi.org/10.1007/978-3-540-39930-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05767-0

  • Online ISBN: 978-3-540-39930-8

  • eBook Packages: Springer Book Archive

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