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Due to the physical proximity and other factors such as wind, local pollination can
have a significant fraction p in the overall pollination activities.
These rules can be translated into the following updating equations:
:<math> x_i^{t+1}=x_i^t + L (x_i^t-g_*)</math>
:<math> x_i^{t+1}=x_i^t + \epsilon (x_i^t-x_k^t)</math>
where <math>x_i^t</math> is the solution vector and <math>g_*</math> is the current best found so far during iteration. The switch probability of two equations is p. In addition, <math>\epsilon></math> is a random number drawn from a uniform distribution. L is a step size drawn from a Levy distribution.
A matlab demo program is available for function optimization<ref>X. S. Yang,http://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm</ref>
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