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Global optimization method based on group abstract convex lower bound supporting surface

A global optimization and convex lower bound technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as low computational complexity, high computational complexity, and low reliability

Inactive Publication Date: 2014-05-14
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the disadvantages of high computational complexity and low reliability of the existing global optimization method, the present invention proposes a support surface based on group abstract convex lower bound with high reliability, low computational complexity and fast convergence speed The global optimization method of

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  • Global optimization method based on group abstract convex lower bound supporting surface
  • Global optimization method based on group abstract convex lower bound supporting surface
  • Global optimization method based on group abstract convex lower bound supporting surface

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[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] refer to Figure 1 ~ Figure 3 , a global optimization method based on the group abstract convex lower bound support surface, including the following steps:

[0064] 1) Parameter initialization: set the constant M, the gain constant F, the crossover probability CR, the population size PopSize, and the upper bound a of each variable i , lower bound b i ;

[0065] 2) Establish an n-ary tree to save the estimated values ​​of the lower bounds:

[0066] 2.1) Convert the vertices of the unit simplex area S according to the formula (1) to obtain the point x 1 ,x 2 ,...,x N+1 ;

[0067] x i = x i ′ Σ i = 1 N ( b i ...

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Abstract

A global optimization method based on a group abstract convex lower bound supporting surface. The method includes the following steps that firstly, the abstract convex lower bound supporting surface is built for an initial group; secondly, in an updating link, an invalid region is removed through lower bound information, so that the number of evaluation times of functions is reduced, and reliability of algorithms is improved; meanwhile, local reinforcement is conducted through the descending direction of the supporting surface, and therefore the convergence rate of the algorithm at the later period is further increased; lastly, the supporting surface is updated according to evolution information. The global optimization method based on the group abstract convex lower bound supporting surface is high in reliability, low in calculation complexity and high in convergence rate.

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 the group abstract convex lower bound support surface. Background technique [0002] In practical engineering applications, many optimization problems often require a global optimal solution. Gradient-based quasi-Newton method, conjugate gradient method and other traditional methods, as well as direct search methods such as Nelder-Mead and Hooke-Jeeves are essentially a kind of local search method, the quality of the solution directly depends on the selection of the starting point, for some For complex optimization problems, it is basically impossible for these methods to obtain the global optimal solution of the problem. [0003] In general, global optimization methods are divided into deterministic methods and stochastic methods. The deterministic method uses the analytical nature of the problem to generate...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 张贵军周晓根秦传庆郝小虎张贝金明洁刘玉栋李章维
Owner ZHEJIANG UNIV OF TECH
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