Design method based on worst case genetic algorithm

A genetic algorithm and worst-case technology, applied in the field of genetic algorithm, can solve problems such as low computational cost, inability to solve non-derivative information, and inability to fully describe the model, achieving the effect of low computational cost and wide application range.

Inactive Publication Date: 2021-05-07
DONGGUAN UNIV OF TECH +2
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Problems solved by technology

However, this model cannot fully describe the real situation, so the model under continuous variables is widely accepted
As for the optimization model under the completely continuous worst case, there are already surrogate model-based optimization methods proposed in articles such as "Anew expected-improvement algorithm for continuous minimax optimization", but due to the large calculation consumption of this method, it is practical Application is still difficult
For simpler worst-case optimization, "Evolutionary Algorithms for MinimaxProblems in Robust Design" proposes to use some evolutionary algorithms

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  • Design method based on worst case genetic algorithm

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[0029]The present invention will be further described below with reference to the accompanying drawings:

[0030]S1. Pre-establish the worst case optimization model of the required solution, the model includes:

[0031]It is necessary to maximize design vector Y to describe the worst case;

[0032]Ultimate design is required to describe objects that require optimization;

[0033]Design vector corresponding to the range;

[0034]A predetermined accuracy of the model solve;

[0035]S2. According to the set value range, randomly generate one maximizing design vector Y1, which should constitute a design variable space of the model, generate set my = {y1}; randomly generate n extreme small design vectors (X1, x2, ..., xn), which should constitute a design variable space of the model, generate set MX = {x1, x2, ..., xn};

[0036]S3. Set the maximized design vector Y in the model to each of my vectors, using S1 improved genetic algorithm for each minimized design vector X in S2 for P sub-optimization and updat...

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Abstract

The invention discloses a design method based on a worst case genetic algorithm, and relates to the field of digital filter design. According to the invention, the genetic algorithm is improved, and the optimization method under the worst condition is obtained. Firstly, the method improves a screening mode of an optimal individual in an existing genetic algorithm during solving, and obtains a method for evaluating the optimal individual under the worst condition for a single time. Secondly, the disadvantage that a gradient information-based algorithm is easy to fall into local optimum and the problem that the solution without derivative or information shortage cannot be handled are effectively avoided by means of crossover variation and the like of a genetic algorithm. The algorithm is low in calculation cost in digital filter design and wide in application range, and a better method is provided for optimization under more general conditions.

Description

technical field [0001] The invention relates to the field of genetic algorithms, in particular to a design method based on worst-case genetic algorithms. Background technique [0002] The digital filter digitally filters out the undesired signal in the signal transmission process and retains the desired signal part, so as to realize the purpose of information extraction and utilization in digital signal processing. Digital filters are the basic operating units of digital signal processing systems, and have been widely used in signal processing in industries and fields such as digital audio-visual equipment, aerospace, and medical systems. However, the current digital filter design does not consider the frequency response approximation error in the transition band, resulting in the filter amplitude response often has obvious overshoot in the transition band, and the group delay error at the edge of the passband is also large. Therefore, the design for minimizing the maximum ...

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Application Information

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IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 俞水李耘何泰霖袁志远史玉洁
Owner DONGGUAN UNIV OF TECH
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