Pipeline multi-objective optimization method and device based on crown-hedgehog-A star nesting

By using a nested multi-objective optimization method based on the Crown Pig-A star, the pipeline design is optimized, solving the problems of pipeline routing relying on manual sequence and space occupation in the existing technology, and achieving shorter and more reasonable pipeline paths and efficient space utilization.

CN121256965BActive Publication Date: 2026-06-05CHINA NORTH ENGINE INST TIANJIN
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA NORTH ENGINE INST TIANJIN
Filing Date
2025-12-04
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing pipeline design methods rely on manually set path-finding sequences, making it difficult to achieve global path optimization. Furthermore, they do not consider the space occupied by different pipeline schemes in the assembly, affecting the performance and assembly efficiency of the power system.

Method used

A multi-objective optimization method based on the nested hog-A-star algorithm is adopted. The initial population is generated by defining a data structure and preference function. The A-star algorithm is used for three-dimensional spatial pipeline routing. The population position is updated by combining the fitness function and the improved hog algorithm to optimize the pipeline path and space utilization.

Benefits of technology

The optimization of the global pipeline path has been achieved, resulting in a shorter and more reasonable pipeline path, which improves space utilization and is beneficial to the layout of the power compartment.

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Abstract

The application provides a pipeline multi-objective optimization method and device based on a lemming-A star nesting, comprising the following steps: defining a data structure to determine all pipeline properties, discretizing a three-dimensional assembly space based on the pipeline properties, and constructing a preference function; generating an initial lemming population through the preference function, performing three-dimensional space pipeline routing on each lemming in the initial lemming population by using a preset A star algorithm to obtain a total pipeline length and a power assembly envelope volume; constructing a fitness function according to the total pipeline length and the power assembly envelope volume, and calculating the fitness value of each lemming through the fitness function; updating the position of each lemming and forming a new lemming population through an improved lemming algorithm according to the fitness value of each lemming, and outputting the position of the new lemming population to obtain a three-dimensional optimal routing path. The application quickly determines an optimal pipeline routing path, and the pipeline routing path has a small space occupation volume, which is beneficial to the arrangement of a power cabin.
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