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Micro electro mechanical system design optimization method based on integrated model assisted social learning particle swarm algorithm

A particle swarm algorithm and MEMS technology, applied in the field of MEMS design optimization to reduce risks

Active Publication Date: 2021-03-19
GUANGDONG UNIV OF TECH
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Problems solved by technology

The integrated model assisted social learning particle swarm algorithm designed by the present invention can not only obtain high-quality design solutions, but also reduce the risk caused by poorly fitting agent models, so the algorithm of the present invention can be applied to different types of The MEMS optimization problem

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  • Micro electro mechanical system design optimization method based on integrated model assisted social learning particle swarm algorithm
  • Micro electro mechanical system design optimization method based on integrated model assisted social learning particle swarm algorithm
  • Micro electro mechanical system design optimization method based on integrated model assisted social learning particle swarm algorithm

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Embodiment Construction

[0064] The present invention will be further described below in conjunction with specific embodiment:

[0065] Such as figure 1 As shown, a MEMS design optimization method based on integrated model-assisted social learning particle swarm algorithm described in the embodiment of the present invention includes the following steps:

[0066] S1. Use the Latinhypercube sampling method to initialize the entire population according to the scope of the design space, and evaluate the real fitness of the individuals in the entire population, and give them real fitness values; the initialized individuals and their fitness values ​​are stored in a cache. forming an initial candidate set DB;

[0067] S2. Judging whether the samples in the candidate set DB are greater than or equal to the number N of samples of the constructed candidate set A, if the judgment condition of greater than or equal to that is not met, then use the social learning particle swarm optimization algorithm to evolve ...

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Abstract

The invention discloses a micro electro mechanical system design optimization method based on an integrated model assisted social learning particle swarm algorithm, which comprises the steps of initializing a population according to a control range of a design variable of an MEMS; selecting a proper amount of samples from the candidate set as a training set according to a selection rule to train an integration model; then, solving the population by using a social learning particle swarm optimization algorithm, and giving a fitness prediction value of each individual by an integrated model; finally, using an SMIC point adding criterion for managing the model, wherein a proper individual is selected for real fitness evaluation. In each iteration, the optimal position is searched from the candidate set cache, and when the iteration is ended, the finally stored position of the optimal individual is used as the optimal variable combination in the MEMS design. Not only can a high-quality design solution be obtained, but also the risk caused by an agent model with poor fitting can be reduced, and the method is suitable for optimization of different types of MEMS.

Description

technical field [0001] The invention relates to the technical field of micro-electro-mechanical system design optimization, in particular to a micro-electro-mechanical system design optimization method based on an integrated model-assisted social learning particle swarm algorithm. Background technique [0002] Micro-Electro-Mechanical System (MEMS) is a micro-device that integrates micro-actuators, micro-sensors, signal processing and control circuits, communications, and interfaces. MEMS has broad application prospects in the fields of medical treatment, industry, automobile and aviation, which plays an important role in promoting national economic growth and improving military capabilities. Therefore, the optimal design of the shape of MEMS is essential and has Great practical significance. [0003] At present, there are two methods of MEMS shape optimization: the first is to use design expertise for local optimization, first provide an initial design, then narrow the sea...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/00G06F115/04
CPCG06F30/27G06N3/006G06F2115/04
Inventor 胡晓敏苏文伟李敏
Owner GUANGDONG UNIV OF TECH
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