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Optimal Design Method of Gear Reducer Based on Clustering Multi-objective Distribution Estimation Algorithm

A distribution estimation algorithm, gear reducer technology, applied in design optimization/simulation, geometric CAD, special data processing applications, etc., can solve the problems of easy loss of population diversity, insufficient use of algorithm local search ability, etc., to achieve enhanced search Effect

Active Publication Date: 2020-08-11
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] The purpose of the present invention is to solve the problem that the existing multi-objective distribution estimation algorithm does not fully utilize the local search ability of the algorithm in the process of solving the multi-objective optimization problem. The calculation cost is used to construct the optimal probability model, and an optimal design method of gear reducer based on clustering multi-objective distribution estimation algorithm is proposed

Method used

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  • Optimal Design Method of Gear Reducer Based on Clustering Multi-objective Distribution Estimation Algorithm
  • Optimal Design Method of Gear Reducer Based on Clustering Multi-objective Distribution Estimation Algorithm
  • Optimal Design Method of Gear Reducer Based on Clustering Multi-objective Distribution Estimation Algorithm

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specific Embodiment approach 1

[0074] Specific implementation mode 1: The specific process of the gear reducer optimization design method based on the clustering multi-objective distribution estimation algorithm is as follows:

[0075] EDA has been widely used in the solution of MOP. Bosman and Thierens (Bosman PA, ThierensD. Multi-objective optimization with diversity preserving mixture-based density estimation evolutionary algorithms [J]. International Journal of Approximate Reasoning, 2002, 31 (3): 259-289) proposed a mixture-based Multi-objective iterative density estimation evolutionary algorithm (MIEDA), for solving continuous and discrete optimization problems, MIEDA is considered as a benchmark algorithm for other MEDAs. Pelikan et al. (Pelikan M, Sastry K, Goldberg D E. Multiobjective hBOA, clustering, and scalability [C]. Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. ACM, 2005: 663-670) adopted a dominance-based framework and used K-means clustering algorithm i...

specific Embodiment approach 2

[0092] Specific embodiment two: the difference between this embodiment and specific embodiment one is: AHC (P, K) in the step two and two is specifically:

[0093] Use the AHC algorithm to divide the N individuals contained in the population P, that is, P={x 1 ,x 2 ,...,x N}, the principle of dividing into K classes is the following steps.

[0094] (1) Take each individual in the population P as a class;

[0095] (2) Loop:

[0096] (2.1) Calculate the Euclidean distance between every two different classes;

[0097] (2.2) Find out the two classes with the smallest distance and merge them into a new class;

[0098] (2.3) Determine whether the termination condition is met, that is, whether the number of clusters is greater than K, and if so, terminate and output the final clustering result, otherwise go to step (2.1).

[0099] AHC first regards each individual as a class, and then uses a series of mechanisms to merge different classes until the number of population clusters...

specific Embodiment approach 3

[0101] Specific implementation mode three: the difference between this implementation mode and specific implementation mode one or two is: the concrete process that new solution produces in described step 25 is:

[0102] For each individual x i ∈P,i=1,...,N proceed as follows:

[0103] Step 251: For individual x i Choose a covariance matrix Σ i ;

[0104]

[0105] which stated for individual x i The covariance matrix of the local class in which, Σ GC is the covariance matrix of the global class;

[0106] Step 252: Generate a new individual y i =SolGen(Σ i ,x i );

[0107] Step 253: keep the new solution A=A∪{y i}.

[0108] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to a method for optimum design of gear reducers on the basis of a clustering multi-objective estimation of distribution algorithm, and aims at solving the problems that the local search ability of the existing multi-objective estimation of distribution algorithm is not sufficiently utilized in the multi-objective optimization problem solving process, abnormal solutions are directly discarded in the solving process, the population diversity is easy to lose and too much calculation overhead is used for constructing an optimum probability model. The method comprises the following steps of: firstly dividing a population into a plurality of local classes by utilizing an agglomerative hierarchical clustering algorithm; randomly selecting a unity from each local class to form a global class; and constructing a Gaussian model for each unity to approach a population structure and carrying out sampling to generate a new unity, wherein the mean value of the Gaussian model is the unity, and a covariance matrix is a covariance matrix of the local class where the unity is located or a covariance matrix of the global class. The method disclosed by the invention is used for the field of spaceflight.

Description

technical field [0001] The invention relates to an optimal design method of a gear reducer. Background technique [0002] There are a large number of complex multi-objective optimization problems (Multiobjective Optimization Problem, MOP) with multi-constraint, multi-variable and nonlinear properties in practical engineering. A typical constrained MOP is expressed as follows (Wang Yong, Cai Zixing, Zhou Yuren, et al. Evolutionary Algorithms for Constrained Optimization [J]. Journal of Software, 2009, 20(1): 11-29): [0003] minF(x)=[f 1 (x), f 2 (x),...,f m (x)] T [0004] [0005] x=(x 1 ,x 2 ,...,x n ) T ∈Ω [0006] Among them, x is the n-dimensional decision variable vector; F(x) is the m-dimensional objective function vector; p is the inequality constraint g i (x) number; q is the equality constraint h j (x) number. Ω is the decision space. [0007] Since the sub-objectives in MOP conflict with each other in most cases, there is no optimal solution to ma...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/17G06F30/20
CPCG06F30/17G06F30/20
Inventor 宋申民张秀杰高肖霞张虎赵杰
Owner HARBIN INST OF TECH
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