A method of transforming multi-point optimization into multi-objective optimization

Abstract

The invention discloses a method for converting multi-point optimization into multi-target optimization. The method comprises the following steps: (1) on the basis of a selected optimization strategy, generating one group of design variables x; (2) successively according to a plurality of set working conditions, calling a solver to obtain one group of solving values; (3) converting multiple points into multiple targets, processing the solving value of a weighted mean target and a weighted standard deviation target, converting into a final target function value fx; and (4) judging whether a termination condition is met or not, and returning an optimal solution set if the termination condition is met, and otherwise, carrying out execution in sequence from the step (1). Through the method for converting multi-point optimization into multi-target optimization, the multi-target optimization of the weighted mean target and the weighted standard deviation target under one group of working conditions is simultaneously considered, and reliable design optimization can be carried out on a performance curve or under a circumstance of multiple working conditions. The obtained scheme or design is more reliable, product performance is effectively improved, and losses can be lowered.

Description

Technical field [0001] The invention relates to the field of computer technology, in particular to a method for converting multi-point optimization into multi-objective optimization. Background technique [0002] In life, many problems are composed of multiple goals that conflict and influence each other. People often encounter optimization problems that are used in a given area at the same time as the best possible optimization problem, that is, multi-objective optimization problems, optimization goals that exist in optimization problems When there are more than one and need to be processed at the same time, the problem becomes a multi-objective problem. In practice, most optimization problems are multi-objective optimization problems. Generally, the sub-objectives of multi-objective optimization problems are contradictory. The improvement of one sub-objective may cause the performance of another or several sub-objectives to decrease. That is to say, it is impossible to achieve...

AI Technical Summary

Problems solved by technology

Due to a design variable cluster Under multi-working conditions, there will be loss coefficients under multiple working conditions that need to be minimized at the same time. This method will inevitably sacrifice the margin information of the performance curve, and cannot expand the margin of variable working conditions while multi-point performance parameters

[0007] At present, the optimization mechanism that relies on program automation to transform multi-point optimization into multi-objective optimization is still blank, and it is particularly important to solve this kind of problems with practical needs.

Images