A Method for Solving High Dimensional Optimization Problems Based on Approximate Model and Differential Evolution Algorithm

Abstract

The invention belongs to the technical field related to design optimization, and discloses a method for solving high-dimensional optimization problems based on an approximate model and a differential evolution algorithm. The method includes the following steps: (1) determining the design space according to the actual engineering optimization problem to be optimized; 2) Construct the global radial basis function approximation model and the local radial basis function approximation model of all individuals in the current population based on the obtained sample points; (3) Based on the differential evolution algorithm, use the local radial basis function approximation model as a guide to The mutation operation is performed on the current population, and then the crossover operation is performed on the obtained population; and the global radial basis function approximation model is used as a guide to select the population; (4) to judge whether the differential evolution algorithm is convergent, and the convergence is to output the differential evolution algorithm. Calculate the optimal solution, otherwise go to step (2) until the differential evolution algorithm converges. The invention improves the precision of optimization, has stronger applicability and better flexibility.

Description

technical field [0001] The invention belongs to the technical field related to design optimization, and more specifically relates to a method for solving high-dimensional optimization problems based on an approximate model and a differential evolution algorithm. Background technique [0002] Engineering optimization problems usually involve computationally expensive simulations and a large number of design variables, although with the development of computing technology, simulation software on finite element analysis (FEA) and computational fluid dynamics (CFD) can alleviate the computational cost in the design process, However, excessive calculation time will still lead to some complex engineering design problems that cannot be optimized. How to solve these problems in an efficient manner remains a great challenge. [0003] In recent years, approximate model-assisted metaheuristic algorithms have been widely studied and are considered to have the potential to solve such en...

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Problems solved by technology

[0003] In recent years, approximate model-assisted metaheuristic algorithms have been widely studied and are considered to have the potential to solve such engineering optimization problems. Approximate models have considerable advantages in reducing computational costs, but the usual approximate model-assisted metaheuristics The algorithm cannot meet the accuracy requirements of high-dimensional problems with large-scale variables

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