Global optimization method based on strategy adaptability differential evolution

A technology of differential evolution and global optimization, applied in genetic models, genetic laws, data processing applications, etc., to achieve the effect of smooth transition

Inactive Publication Date: 2016-06-15
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0007] Therefore, the existing global optimization method based on differential evolution algorithm has defects in strategy selection and needs to be improved.

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  • Global optimization method based on strategy adaptability differential evolution
  • Global optimization method based on strategy adaptability differential evolution
  • Global optimization method based on strategy adaptability differential evolution

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] refer to figure 1 and figure 2 , a global optimization method based on policy adaptive differential evolution, including the following steps:

[0044] 1) Initialization: set the population size N P , the initial crossover probability C R , the initial gain constant F;

[0045] 2) Randomly generate the initial population P={x 1,g ,x 2,g ,...,x Np,g}, and calculate the objective function value of each individual, where g is the evolution algebra, x i,g ,i=1,2,...,Np represents the i-th individual in the g-th generation population, if g=0, it represents the initial population;

[0046] 3) According to each individual x i,g The objective function value f(x i,g ) Sort each individual in descending order, and record the rank F of each individual i,g , and find the optimal individual x in the current population best,g , where F i,g Indicates the ranking ...

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Abstract

A global optimization method based on strategy adaptive differential evolution. First, calculate the distance between each individual in the population and the optimal individual in the current population, and rank the entire population according to the distance and objective function value; then, according to The average error value of distance ranking and objective function value ranking judges the distribution of individuals in the current population, and then judges the state of the algorithm search, that is, global detection and local search, and sets multiple different mutation strategies for each search state; Finally, for each individual in the population, a mutation strategy is randomly selected from its corresponding state strategy pool to generate a new individual, so as to achieve the effect of balancing the algorithm's global detection ability and local enhancement ability. The invention effectively avoids improper strategy selection from affecting the performance of the algorithm and improves the optimization performance.

Description

technical field [0001] The invention relates to the field of intelligent optimization and computer application, in particular to a global optimization method based on strategy adaptive differential evolution. Background technique [0002] Some global optimization problems are often encountered in the fields of economy, science and engineering. In global optimization, the algorithm needs to find a global optimal solution from many local optimal solutions. However, the biggest problem for these global optimization algorithms is It may fall into a local optimum and cannot obtain a global optimum solution. With the increasing complexity of engineering optimization problems, the behavior of the objective function of the optimization problem is also becoming more and more complex, usually discontinuous, non-differentiable, highly nonlinear, without a clear analytical expression, and has multiple peaks , Multi-target features. Therefore, traditional optimization methods such as g...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12
CPCG06Q10/04G06N3/126
Inventor 张贵军周晓根俞旭锋郝小虎徐东伟李章维
Owner ZHEJIANG UNIV OF TECH
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