Superconducting Cable Parameter Optimization for Faster Global Convergence
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Solution Overview
Problem
Conventional optimization methods for superconducting cable structures face challenges in convergence speed and accuracy due to premature local optima and increased calculation time, especially when dealing with complex multi-modal function optimization problems and large-scale parameters.
Innovation Solution
An improved multi-objective grey wolf optimization algorithm with weight coefficients based on overall sensitivity indices is employed to iteratively solve the multi-objective optimization model of superconducting cable structural parameters, adjusting iteration coefficients for different parameters to enhance search accuracy and avoid local optima.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the particle swarm optimization algorithm is used to optimize superconducting cable structural parameters, then the convergence speed is improved, but the algorithm may fall into local optimal solutions and fail to find the global optimal solution when dealing with complex multi-modal function optimization problems
Solution Approach 1:
The patent divides the particle swarm optimization algorithm into multiple sub-swarms, each responsible for exploring different regions of the search space. This segmentation allows the algorithm to maintain fast convergence while reducing the risk of falling into local optima by distributing the search effort across multiple independent groups that can exchange information.
Solution Approach 2:
The patent introduces an intermediary mechanism that monitors and adjusts the optimization process. This intermediary component detects when particles are converging to local optima and triggers corrective actions such as perturbing particle positions or adjusting velocity parameters, thereby maintaining the balance between convergence speed and finding the global optimum.
2Adaptability or versatility
If the MOGWO algorithm is used to process the structure optimization model of superconducting cable, then the diversity of solution space is maintained, but the calculation time increases exponentially when dealing with more tape layers and parameters
Solution Approach 1:
The patent applies partial action by selectively optimizing only the most critical parameters of the superconducting cable structure using the MOGWO algorithm, while keeping other parameters fixed or using simpler optimization methods. This reduces the dimensionality of the optimization problem and significantly decreases calculation time while maintaining solution space diversity for the key parameters.
Solution Approach 2:
The patent performs preliminary analysis to identify and fix parameters that have minimal impact on the objective functions before applying the MOGWO algorithm. This preliminary action reduces the number of parameters that need to be optimized, thereby reducing calculation time while preserving the algorithm's ability to maintain diverse solutions for the critical parameters.
Data Source
AI summary
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.