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A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing.

Ziqi Liu1, Yong Xue2, Jiaqi Zhao1

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Biomimetics (Basel, Switzerland)
|November 26, 2024
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Summary
This summary is machine-generated.

A new Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm enhances remote sensing data processing efficiency. It significantly reduces task completion time and optimizes resource allocation for better computing performance.

Keywords:
Siberian Tiger Optimizationmetaheuristicoptimal assignmentremote sensing datatask schedulingworkflow

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Area of Science:

  • Earth and Space Sciences
  • Computer Science
  • Data Science

Background:

  • Exponential growth in remote sensing data overwhelms traditional computing models.
  • Efficient processing of vast geospatial datasets is critical for global observation.
  • Distributed cluster computing task scheduling impacts completion time and resource utilization.

Purpose of the Study:

  • To enhance task processing efficiency for remote sensing data.
  • To optimize the allocation of computing resources in distributed environments.
  • To develop an improved optimization algorithm for task scheduling.

Main Methods:

  • Development of the Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm.
  • Integration of Tent chaotic map, Lévy flight, Cauchy mutation, and learning strategies.
  • Application of stochastic key and uniform allocation encoding schemes for task scheduling.

Main Results:

  • MSSTO algorithm demonstrates superior convergence speed and global optimal solution search.
  • Task completion time reduced by 21% compared to the original STO algorithm.
  • Achieved an average 15% reduction in completion time compared to nine advanced algorithms.

Conclusions:

  • MSSTO algorithm significantly improves task processing efficiency and resource allocation.
  • The proposed method offers superior solution accuracy and convergence speed for task scheduling.
  • Optimal execution sequences and machine allocation schemes were achieved for aerosol optical depth retrieval.