Robust optimization method based on average gradient value and improved multi-objective particle swarm optimization

Abstract

The invention relates to a robust optimization method based on an average gradient value and improved multi-objective particle swarm optimization. The robust optimization method comprises the following steps: (1) determining technological parameters to be optimized according to actual engineering situations and an optimization objective, (2) determining constraint conditions of the technological parameters to be optimized according to the actual engineering situations, (3) establishing an objective function model of the optimization objective and the technological parameters to be optimized, (4) obtaining the discrete relation between the optimization objective and welding technological parameters through a finite test, (5) calculating an objective function according to the discrete relation, (6) setting an uncertain region and calculating the robustness through the average gradient value of sample points of the uncertain region, (7) carrying out bi-objective optimization solution with the objective function and the robustness as two optimization sub-objectives. According to the robust optimization method, evaluation of the robustness and calculation of the original objective function are taken as the two optimization sub-objectives at the same time, and therefore a designer can select a reasonable compromise solution between a performance index and the amplitude of variation of the uncertain region according to actual needs.

Description

technical field [0001] The invention relates to the field of computer data processing, in particular to a robust optimization method based on average gradient value and improved multi-objective particle swarm optimization. Background technique [0002] Robust optimization is a new optimization method to solve the uncertain environment of internal structure (such as parameters) and external environment (such as disturbance). It considers the uncertainty of the optimization model at the beginning of optimization. Through the optimization method, the The optimized result is insensitive to uncertain factors and the optimal unity of performance index. The classic robust optimization method is mainly used in operations research, and studies convex problems such as linear programming problems, quadratic programming problems and semidefinite programming problems with different forms of data uncertainty. However, engineering problems in the real world are often non-convex, and even ...

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Problems solved by technology

However, engineering problems in the real world are often non-convex, and even have no mathematical expressions. The classical methods in operations research are not suitable for engineering design.

In existing engineering problems, how to solve a series of robust optimization solutions for practical problems without analytical expressions, highly nonlinear, and high-dimensional decision space has become an urgent problem in the field of robust optimization.

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