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案件記録

MULTI-OBJECTIVE OPTIMIZATION METHOD, DEVICE AND MEDIUM FOR STRUCTURAL PARAMETERS OF SUPERCONDUCTING CABLE

発明審査中
10請求項 · 1 独立
§ Ⅰ

案件概要

発明者

Ting JIAO; Honglei LI; Lei SU; Aiqing MA; Haoyang TIAN; Qingchuan XU; Shunning ZHANG

IPC分類

G6F 30/18H1B 12/2

CPC分類

G6F30/18H1B12/2

A multi-objective optimization method for structural parameters of a superconducting cable, comprising steps of: S1, obtaining structural parameters and performance parameters of the superconducting cable, setting a constraint range of the structural parameters of the superconducting cable, based on the structural parameters and performance parameters of the superconducting cable, constructing an objective function, to establish a multi-objective optimization model of the structural parameters of the superconducting cable; and S2, through an improved multi-objective grey wolf optimization algorithm, iteratively solving the multi-objective optimization model of the structural parameters of the superconducting cable, to obtain an optimal solution for each of the structural parameters of the superconducting cable; where, to an iteration coefficient in a multi-objective grey wolf optimization algorithm, a weight coefficient negatively correlated with an overall sensitivity index of each of the structural parameters of the superconducting cable is given, to obtain the improved multi-objective grey wolf optimization algorithm.

原文(中国語)

A multi-objective optimization method for structural parameters of a superconducting cable, comprising steps of: S1, obtaining structural parameters and performance parameters of the superconducting cable, setting a constraint range of the structural parameters of the superconducting cable, based on the structural parameters and performance parameters of the superconducting cable, constructing an objective function, to establish a multi-objective optimization model of the structural parameters of the superconducting cable; and S2, through an improved multi-objective grey wolf optimization algorithm, iteratively solving the multi-objective optimization model of the structural parameters of the superconducting cable, to obtain an optimal solution for each of the structural parameters of the superconducting cable; where, to an iteration coefficient in a multi-objective grey wolf optimization algorithm, a weight coefficient negatively correlated with an overall sensitivity index of each of the structural parameters of the superconducting cable is given, to obtain the improved multi-objective grey wolf optimization algorithm.

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