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Related Experiment Video

Updated: Jan 10, 2026

Precision Milling of Carbon Nanotube Forests Using Low Pressure Scanning Electron Microscopy

Precision Milling of Carbon Nanotube Forests Using Low Pressure Scanning Electron Microscopy

Published on: February 5, 2017

Enhanced kernel search algorithm for optimizing local search capability and its application to carbon fiber draft

Ruyi Dong¹, Ran Cui¹, Zhennao Cai²

¹College of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.

|November 26, 2025

Summary

This summary is machine-generated.

A new Large Local Search Kernel Search Optimization (LLSKSO) algorithm improves local search accuracy. LLSKSO enhances optimization performance and shows superior results in benchmark tests and engineering applications.

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Area of Science:

Optimization Algorithms
Computational Intelligence
Metaheuristics

Background:

Kernel Search Optimization (KSO) suffers from limited local search accuracy, hindering effective local optimization.
Existing optimization methods often struggle to balance local exploitation and global exploration.

Purpose of the Study:

To introduce a novel Large Local Search Kernel Search Optimization (LLSKSO) algorithm.
To enhance the local search capability and overall performance of KSO.

Main Methods:

Homogenizing the initial population using the good point set mechanism.
Integrating the Dung Beetle Optimizer's (DBO) search mechanism to boost local search.
Employing Cauchy-Gaussian mutation to escape local optima and maintain a balance between local and global search.

Main Results:

LLSKSO demonstrated superior performance over 10 established algorithms across 50 benchmark functions.
The algorithm achieved better statistical results in mean, best, and variance.
LLSKSO achieved theoretical optima on 16 out of 20 high-dimensional functions, reducing CPU runtime by 30%.

Conclusions:

LLSKSO effectively enhances local optimization capabilities and achieves a dynamic balance between local and global search.
The algorithm shows significant improvements in engineering applications, such as carbon fiber drafting ratio optimization.
LLSKSO is a robust and efficient optimization algorithm suitable for complex engineering problems.