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Related Experiment Video

Updated: May 27, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Cloud Resource Scheduling Using Multi-Strategy Fused Honey Badger Algorithm.

Haitao Xie¹, Chengkai Li², Zhiwei Ye¹

¹School of Computer Science, Hubei University of Technology, Wuhan, China.

|February 19, 2025

Summary

This summary is machine-generated.

An Improved Honey Badger Algorithm (IHBA) enhances cloud resource scheduling by preventing local optima. This novel approach improves task allocation and load balancing in big data environments.

Keywords:

Honey Badger Algorithm analytics and visualization big data cloud resource scheduling load balancing

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Area of Science:

Computer Science
Artificial Intelligence
Big Data Analytics

Background:

Cloud resource scheduling is a critical combinatorial optimization problem in big data.
Meta-heuristic algorithms (MAs) are commonly used but often suffer from local optima, reducing allocation scheme quality.

Purpose of the Study:

To propose an Improved Honey Badger Algorithm (IHBA) for superior cloud resource scheduling.
To enhance global search capabilities and mitigate local optima issues in scheduling.

Main Methods:

Developed IHBA by integrating two local search strategies and a novel fitness function.
Compared IHBA against six other MAs across four diverse load-scale tasks.

Main Results:

IHBA demonstrated superior performance compared to the other six MAs.
The algorithm effectively improved population diversity and expanded search ranges.
IHBA successfully prevented local optima and achieved efficient resource load balancing.

Conclusions:

IHBA offers a significant advancement in cloud resource scheduling algorithms.
The proposed method provides a robust solution for optimizing big data resource allocation.