MULTI-OBJECTIVE OPTIMIZATION METHOD, DEVICE AND MEDIUM FOR STRUCTURAL PARAMETERS OF SUPERCONDUCTING CABLE
卷宗概要
申请人
STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO.
发明人
Ting JIAO; Honglei LI; Lei SU; Aiqing MA; Haoyang TIAN; Qingchuan XU; Shunning ZHANG
IPC 分类
CPC 分类
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（中文）
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.