Discrete variable structural optimization based on multidirectional fuzzy genetic algorithm

Abstract

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures; however, it has some disadvantages such as poor precision, simple model and lacking of working conditions’ description, etc. To solve these problems, a new model is constructed by defining parameterized fuzzy entropy, and the rationality of parameterized fuzzy entropy is verified. And a new multidirectional searching algorithm is further put forward, which takes information of actual working conditions into consideration and has a powerful local searching capability. Then this new algorithm is combined with the GA by the fuzzy clustering algorithm (FCA). With the application of FCA, the optimal solution can be effectively filtered so as to retain the diversity and the elite of the optimal solution, and avoid the structural re-analysis phenomenon between the two algorithms. The structure design of a high pressure bypass-valve body is used as an example to make a structural optimization by the proposed HGA and finite element method (FEM), respectively. The comparison result shows that the improved HGA fully considers the characteristic of discrete variable and information of working conditions, and is more suitable to the optimal problems with complex working conditions. Meanwhile, the research provides a new approach for discrete variable structure optimization problems.