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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.

Pending Publication Date: 2020-12-29
CRRC CHANGCHUN RAILWAY VEHICLES CO LTD
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AI Technical Summary

Problems solved by technology

However, for most intelligent bionic algorithms, such as genetic algorithms, as the evolution process continues, the population diversity gradually decreases, the probability of falling into a local optimum is high, and the optimal solution is easily lost.

Method used

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  • IJAYAGA algorithm based on wavelet variation
  • IJAYAGA algorithm based on wavelet variation
  • IJAYAGA algorithm based on wavelet variation

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Embodiment Construction

[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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Abstract

According to the technical scheme, a IJAYAGA algorithm based on wavelet variation has the advantages of being clear in principle and simple in design, the IJAYAGA algorithm based on wavelet variationis adopted for solving the problems that an optimization problem in the engineering optimization field is low in solving speed and prone to falling into a local optimal solution in the solving process, the improved JAYA algorithm is used for guiding the solution updating direction. Updating in the inferior solution direction is avoided, and solving speed is increased; wavelet variation is used forincreasing population diversity in the population variation process, an optimal population is selected in a competitive mode to eliminate bad variation individuals, and therefore the diversity of solutions is increased, the probability that the solutions fall into local optimum in the solving process is reduced, and the solutions are updated in the global optimum solution direction at a high speed. A reliable and rapid solution optimization scheme is provided for engineering optimization problems of traffic flow prediction, traffic scheduling and the like.

Description

technical field [0001] The invention relates to the field of intelligent algorithm optimization, in particular to an adaptive regeneration genetic algorithm based on population similarity. Background technique [0002] Optimization problem is one of the main problems in engineering practice and scientific research. The solution to the optimization problem mainly includes two methods: analytical method and intelligent bionic algorithm: [0003] The analytical method is only applicable when there are obvious analytical expressions for the objective function and constraints. The solution method is: first find out the optimal necessary conditions, obtain a set of equations or inequalities, and then solve this set of equations or inequalities, generally use the derivative method or variational method to find out the necessary conditions, and simplify the problem through the necessary conditions . However, optimization problems in real life are more complicated or cannot be des...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 李恩龙袁志鹏李振龙赵海波
Owner CRRC CHANGCHUN RAILWAY VEHICLES CO LTD
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