Data optimization method, device, apparatus and storage medium

By embedding differential evolution and reverse learning strategies into NSGA-II, the problems of low search efficiency and insufficient diversity in suspension system optimization are solved, enabling efficient design and performance improvement of suspension systems under multiple operating conditions.

CN121389825BActive Publication Date: 2026-07-24CHENGDU GONGDING TECHNOLOGY CO LTD +2
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Patent Information

Application Number
CN202511959606.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-24
Publication Date
2026-07-24
Estimated Expiration
2045-12-24

AI Technical Summary

Technical Problem

The NSGA-II suffers from problems such as low search efficiency, weak local search capability, insufficient diversity retention, and poor adaptability to multiple operating conditions in suspension system optimization.

Method used

By embedding differential evolution and reverse learning strategies into the NSGA-II framework, a collaborative mechanism of global search and local development is formed. Differential evolution enhances the search directionality, reverse learning expands the search scope, and classification ensures that the output target population dataset contains multiple equivalent design schemes.

Benefits of technology

It improves the multi-condition adaptability and design versatility of the suspension system, shortens the design cycle, and enhances the overall performance of the suspension system.

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Abstract

Embodiments of the present application provide a data optimization method, device and equipment and a storage medium. The method comprises: obtaining an initial population data set, the initial population data set comprising at least one individual data, the individual data comprising at least one design parameter; performing reverse learning processing on the initial population data set to obtain a first population data set; performing differential evolution processing and crossover processing on the first population data set to obtain a second population data set; performing non-dominated sorting processing and clustering processing on the second population data set to obtain a target population data set, the target population data set being used for multi-objective optimization. Through multi-working-condition constraint processing and multi-modal solution set maintenance, the output target population data set can meet the design requirements under different working conditions, provide diversified candidate schemes for engineers, shorten the design cycle and improve the comprehensive performance of the suspension system.
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