IJAYAGA algorithm based on wavelet variation
A wavelet mutation and algorithm technology, applied in genetic models, genetic laws, etc., can solve problems such as high probability of local optima, easy loss of optimal solutions, and reduced population diversity.
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[0058] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0059] In order to illustrate the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and accompanying drawings. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not limit the protection scope of the present invention.
[0060] Such as figure 1 As shown, an IJAYAGA algorithm based on wavelet variation includes the following steps:
[0061] Step0 initialization:
[0062] Design the current iteration number k=0, and the maximum evolution algebra K, randomly generate N individuals as the initial population P(0) according to the variable constraints to be optimized. Set the population individual evaluation function f(X).
[0063] Step1 individual evaluation:...
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