Part lightweight optimization design method based on novel multi-objective particle swarm algorithm

Abstract

The invention discloses a part lightweight optimization design method based on a novel multi-objective particle swarm algorithm, and belongs to the technical field of part lightweight optimization design. The method includes four steps of finite element modeling and analysis, mathematical model establishment, improved multi-target particle swarm optimization solution and optimization result comparison and verification. According to the part lightweight optimization design method based on the novel multi-objective particle swarm optimization, the basic multi-objective particle swarm optimization is improved in order to overcome the conditions that the multi-objective particle swarm optimization cannot highlight different importance of different objective functions, the inertia weight factor robustness is poor, and global optimization is difficult.

Description

technical field [0001] The invention relates to the technical field of component lightweight optimization design, in particular to a component lightweight optimization design method based on a novel multi-objective particle swarm algorithm. Background technique [0002] In recent years, with the increasingly serious energy shortage and environmental crisis, automobile lightweight has become an inevitable trend of automobile development. The basis of vehicle lightweight is the lightweight of components. The forward development technology of lightweight components has become a hot area of ​​research by scholars at home and abroad. At present, many scholars at home and abroad have conducted in-depth research on the optimal design of lightweight parts, fatigue life prediction and reliable durability, but there are still many shortcomings in the theory. How to determine the design scheme of parts, determine the function and structure of parts Parameters, so that the parts have ...

AI Technical Summary

Problems solved by technology

Most of the methods currently used only consider the distribution of the non-inferior solution set, and do not consider the quality of the selected global extremum. How to properly select the global extremum can not only improve the distribution of the non-inferior solution set, but also improve the overall quality. The quality of extremum is the key to the improvement of algorithm performance

[0020] (3) The inertia weight of the particle swarm optimization algorithm affects the balance between the global shrinkage and local search ability of the algorithm. When it is larger, the global search ability is strong, and when it is small, the local search ability is strong. The current proposed method mainly has a linear decay weight to a certain extent. The performance of the algorithm is improved, but the actual search process is often nonlinear

However, the selection of membership function and the implementation of fuzzy inference rules are very complicated and difficult to popularize. Therefore, how to propose a reasonable inertia weight change strategy is the key to balance the local search and global search of the algorithm.

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