Intelligence Semantics

Self-Organizing Migrating Algorithm: Methodology and by Donald Davendra, Ivan Zelinka

By Donald Davendra, Ivan Zelinka

This booklet brings jointly the present kingdom of-the-art learn in Self Organizing Migrating set of rules (SOMA) as a unique population-based evolutionary set of rules, modeled at the predator-prey dating, by way of its major practitioners.

As the 1st ever ebook on SOMA, this ebook is geared in the direction of graduate scholars, lecturers and researchers, who're searching for a very good optimization set of rules for his or her functions. This e-book provides the method of SOMA, overlaying either the genuine and discrete domain names, and its numerous implementations in numerous examine parts. The easy-to-follow and enforce technique utilized in the publication will make it more uncomplicated for a reader to enforce, alter and make the most of SOMA.

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0. If PRT equals 1 then the stochastic component of SOMA disappears and it performs only deterministic behavior suitable for local search. • Dim—the dimensionality (number of optimized arguments of cost function) is given by the optimization problem. Its exact value is determined by the cost function and usually cannot be changed unless the user can reformulate the optimization problem. 2. • PopSize 2 [10, up to the user]. This is the number of individuals in the population. 7 times of the dimensionality (Dim) of the given problem.

Despite this, the SOMA algorithm itself may still work internally with continuous floating-point values. Thus, SOMA—Self-organizing Migrating Algorithm 31 fcost ðyi Þ i ¼ 1; . ; nparam where& for continuous variables xi yi ¼ INTðxi Þ for integer variables xi 2 X ð35Þ INT() is a function for converting a real value to an integer value by truncation. Truncation is performed here only for purposes of cost function value evaluation. Truncated values are not elsewhere assigned. Thus, EA works with a population of continuous variables regardless of the corresponding object variable type.

ISBN 978-3-540-25318-1 26. : Scatter search—methodology and implementations in C. Springer, Verlag. ISBN 978-1-4020-7376-2 (2003) 27. : Particle swarm optimization. ISTE Publishing Company, ISBN (2009). 1905209045 28. 2006 [cit. 20. 2. 2007]. cn/seal06/doc/tutorial_pso. pdf (2006) 29. : A new optimizer using particle swarm theory, pp. 39–43. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya (1995) 30. : Cuckoo search via LŽvy flights. World Congress on Nature and Biologically Inspired Computing (NaBIC 2009).

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