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Niche sorting particle swarm algorithm based dynamic characteristic optimization method for electromagnetic mechanism

A technology of particle swarm algorithm and dynamic characteristics, applied in computing, electrical digital data processing, special data processing applications, etc., can solve problems such as failure to achieve multi-objective optimization, stay in single-objective optimization, etc., and achieve the goal of improving global search capabilities Effect

Active Publication Date: 2015-11-11
HARBIN INST OF TECH
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Problems solved by technology

The optimization algorithms used to solve the dynamic characteristics of electromagnetic mechanisms in most literatures are still in the single-objective optimization stage, and have not achieved real multi-objective optimization.

Method used

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  • Niche sorting particle swarm algorithm based dynamic characteristic optimization method for electromagnetic mechanism
  • Niche sorting particle swarm algorithm based dynamic characteristic optimization method for electromagnetic mechanism
  • Niche sorting particle swarm algorithm based dynamic characteristic optimization method for electromagnetic mechanism

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specific Embodiment approach 1

[0048] Specific implementation mode one: the following combination figure 1 with figure 2 Describe this embodiment, the method for optimizing the dynamic characteristics of electromagnetic mechanisms based on the niche sorting particle swarm algorithm described in this embodiment, it includes the following steps:

[0049] Step 1: According to the design requirements of the dynamic characteristics of the electromagnetic mechanism, determine a group of dynamic characteristic optimization parameters, and determine its dynamic characteristic related indicators according to the dynamic characteristic optimization parameters, and use the dynamic characteristic related indicators as the optimization objective function;

[0050] Step 2: Determine the upper and lower limits of each dynamic characteristic optimization parameter according to the product material and processing technology of the electromagnetic mechanism, and at the same time determine the additional constraint indicators ...

specific Embodiment approach 2

[0056] Specific implementation mode two: the following combination Figure 1 to Figure 4 Illustrate this embodiment, and this embodiment will further explain Embodiment 1. The electromagnetic mechanism described in step 1 is a high-power direct-acting electromagnetic mechanism, and the stiffness k1 of the reaction spring of the high-power direct-acting electromagnetic mechanism The pre-pressure f10 of the rebound spring, the stiffness k2 of the rebound spring and the pre-pressure f20 of the rebound spring are used as dynamic characteristic optimization parameters; the contact closing speed and armature breaking speed are used as the optimization objective function;

[0057] Determine the stiffness k1 of the counterforce spring, the preload f10 of the counterforce spring, the stiffness k2 of the bounce spring and the upper and lower limits of the preload f20 of the bounce spring and the upper and lower limits of the time t are data in table 1, and the time t is used as Addition...

specific Embodiment approach 3

[0074] Specific implementation mode three: the following combination figure 1 with image 3 Describe this embodiment, this embodiment will further explain Embodiment 2, the specific process of obtaining the Pareto solution set distribution of the optimized objective function is:

[0075] Step 1: Input the upper and lower limits of each dynamic characteristic optimization parameter and additional constraint indicators into the multi-objective particle swarm algorithm module; initialize the particle population, randomly generate the initial position and initial velocity of the optimization objective function, and take the local optimal position of the particle as initial position, the outer space is empty;

[0076] Step 2: Then, according to the stiffness k1 of the reaction spring of the high-power direct-acting electromagnetic mechanism, the preload f10 of the reaction spring, the stiffness k2 of the bounce spring and the preload f20 of the bounce spring, the characteristic pa...

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Abstract

Provided is a niche sorting particle swarm algorithm based dynamic characteristic optimization method for an electromagnetic mechanism, and belongs to the technical field of dynamic characteristic optimization of electromagnetic mechanisms. The invention aims to solve the problem that at present, only single-objective optimization can be carried out in an optimization algorithm that is used to solve a dynamic characteristic of the electromagnetic mechanism. The optimization method comprises the steps of: firstly, determining dynamic characteristic optimization parameters and an optimization objective function; secondly, determining upper and lower limits of each dynamic characteristic optimization parameter and an additional constraint index that relates to the dynamic characteristic optimization parameter; thirdly, obtaining initialized data of each dynamic characteristic optimization parameter; fourthly, performing calculation to obtain a corresponding optimization objective function value; fifthly, selecting an individual optimal position of a particle parameter and a global optimal position of a whole population, so as to obtain Pareto solution set distribution of the optimization objective function; and sixthly, carrying out measurement or biased selection on the optimization objective function value in the Pareto solution set distribution obtained in the fifth step. The optimization method is used for dynamic characteristic optimization of the electromagnetic mechanism.

Description

technical field [0001] The invention relates to a method for optimizing the dynamic characteristics of an electromagnetic mechanism based on a niche sorting particle swarm algorithm, and belongs to the technical field of optimizing the dynamic characteristics of an electromagnetic mechanism. Background technique [0002] The optimization of dynamic characteristics of electromagnetic mechanism is a multi-objective, highly nonlinear parameter design problem with constraints. The electrical parameters and mechanical parameters of the electromagnetic mechanism determine the working characteristics and electrical indicators of the electromagnetic mechanism, such as pull-in voltage, pull-in time, collision speed, release speed, etc., and the working characteristics of the electromagnetic mechanism are directly related to the performance and even the electrical life of the electromagnetic mechanism . Therefore, it is of great value and significance to provide effective means and m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
Inventor 梁慧敏邹帆于昊翟国富刘德龙
Owner HARBIN INST OF TECH
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