A truss size optimization design method based on cloud model differential evolution algorithm

Abstract

The invention discloses a truss size optimization design method based on cloud model differential evolution algorithm, which comprises the following steps: in the mutation operation process of a differential evolution algorithm, by using the characteristics that a cloud model simultaneously has certainty and uncertainty as well as stability and variability, generating new individuals in a search space by using an orientated sampling mechanism integrating randomness and stability tendency so as to maintain the diversity of a population, and meanwhile, guiding an evolution operation by using optimal-solution information obtained in the process of search, and fusing multiple male parents to carry out crossed partial search on the operation so as to accelerate the convergence speed of the algorithm; in addition, according to current evolution state information, adaptively dynamically adjusting the value of crossover probability so as to enhance the robustness of the algorithm; and repeatedly executing the steps above until termination conditions are satisfied, so that the optimal individual obtained in the process of calculating is a truss size optimization design result. Compared with similar methods, the method disclosed by the invention can reduce the probability of trapping in local optimum, accelerate the convergence speed, and improve the performance of truss optimization design.

Description

technical field [0001] The invention relates to the field of truss optimization design, in particular to a truss size optimization design method based on cloud model differential evolution algorithm. Background technique [0002] In the optimal design of trusses, it is often necessary to evolve and optimize the size of trusses. Generally, truss size optimization refers to optimizing the cross-sectional area of ​​each group of members under the conditions of given truss structure, material, layout topology and shape, so as to minimize the overall weight of the truss structure, and it is required to meet the specified upper and lower limits of the cross-sectional area value range, and satisfy the stress constraints and displacement constraints of each group of members. In general, the design variable in the truss size optimization design process is the cross-sectional area of ​​the member. In the actual complex truss optimization design applications, many truss optimization ...

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Problems solved by technology

[0004] The present invention mainly solves the technical problems existing in the prior art. Aiming at the shortcomings of the traditional differential evolution algorithm applied to the truss size optimization design, it is easy to fall into the local optimum, the convergence speed is slow, and the optimization design accuracy is not high. A method based on The truss size optimization design method based on the cloud model differential evolution algorithm, the invention can reduce the probability of falling into a local optimum, accelerate the convergence speed, and improve the performance of the truss optimal design

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