Multi-objective sensor distributed point optimizing method on basis of self-adaptive differential evolution

Abstract

The invention discloses a multi-objective sensor distributed point optimizing method on the basis of self-adaptive differential evolution. The multi-objective sensor distributed point optimizing method includes steps of (1) setting up a finite element model of a structure to be measured, and utilizing the numerical solution method to obtain the dynamic characteristic data of the structure; extracting a vibration mode matrix of all alternative measuring points and utilizing all measuring points contained in various step vibration modes as the alternative resource of the optimization; (2) selecting more than two of the modal assurance criterion and various optimization criterions based on the modal strain energy and the measuring vibration displacement maximization as an objective function of the multi-objective constraint sensor optimization of the distributed points, wherein the objective function is used for evaluating advantages and disadvantages of clusters and is the basis for self-adaptive differential evolution algorithm operation, and the selecting process of the objective function is a process determining the optimization criterion; (3) solving the problem of the sensor optimization of the distributed points under the multi-objective constraint by means of the self-adaptive differential evolution algorithm.

Description

technical field [0001] The invention relates to the field of optimal arrangement of structural health monitoring sensors, in particular to a method for optimal arrangement of multi-objective sensors based on adaptive differential evolution. Background technique [0002] Due to the unique shape of the large steel structure building, large span, large component size, and a large number of spatially distorted components, the overall structure is a high-order hyperstatically indeterminate structure, and the force is very complicated. New technologies, new materials, and new processes were adopted in the design. Highly coupled with construction, time-varying structural properties and mechanical properties, environmental loads during operation, fatigue effects, corrosion effects, material aging, and other human factors such as improper use, etc., the structural health monitoring system came into being . [0003] The optimal placement of sensors is the premise of a structural heal...

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Problems solved by technology

[0008] The existing methods have the following problems: 1. Traditional numerical optimization algorithms are poor in robustness, often only local convergence and slow convergence speed

2. There is randomness in optimization; 3. The optimization of sensor layout is based on a single criterion

4. The optimal solution is inaccessible, and the time to find a satisfactory solution is too long

5. Non-adaptive differential evolution algorithm is prone to prema

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