Self-Organizing Migrating Algorithm: Methodology and by Donald Davendra, Ivan Zelinka
By Donald Davendra, Ivan Zelinka
This ebook brings jointly the present nation of-the-art learn in Self Organizing Migrating set of rules (SOMA) as a singular population-based evolutionary set of rules, modeled at the predator-prey courting, through its prime practitioners.
As the 1st ever ebook on SOMA, this e-book is geared in the direction of graduate scholars, teachers and researchers, who're searching for a very good optimization set of rules for his or her functions. This e-book offers the technique of SOMA, masking either the genuine and discrete domain names, and its numerous implementations in several examine components. 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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Additional info for Self-Organizing Migrating Algorithm: Methodology and Implementation
E. can be covered by grid, ﬁne or rough. e. there are no individuals with exactly the same coordinates. This is true every time, because probability that two or more individuals will share the same position is almost 0 (there is an inﬁnite number of real numbers). Discretization can be done a priori by estimation or a posteriori so that after evolution is grid size based on minimal distance of individuals in the population. e. n axes) is probability done by PGE ¼ Neval Ln ð43Þ If grid size is constant, then probability of global extreme retrieval is bigger if Neval increase and vice versa.
When a Specimen is properly deﬁed then the population (Table 2) is generated as follows ð0Þ ðHiÞ Pð0Þ ¼ xi;j ¼ rndi;j ðxj ðLoÞ À xj ðLoÞ Þ þ xj i ¼ 1; . ; nPopSize ; j ¼ 1; . ; mparam ð23Þ Meaning of parameters is following—P(0) is the initial population and x is jth parameter of individual which consist of n parameters. Population then consist of nPopSize individuals. e. that the initial candidate solutions are chosen from that area within the search space that contains a feasible solution to the optimization problem, see Fig.
20 and 21). • Aircraft wing design. The paper deals with a promising approach of modeling the real life systems, characterized with sets of measured/discrete data, by replacing them with analytical functions framework. The article is focused on neural network approximation of functional expressions. As an analyzed system a dynamic flight model has been chosen due to the necessity of considering several classes of large sets of aerodynamic lift, drag, speed, force, balance and Fig. 20 Plasma reactor equipment Fig.