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Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology.

Dongming Yan1, Yue Liu1, Lijuan Li1,2

  • 1School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces the improved remora optimization algorithm (PROA) for efficient large-scale measurement station deployment. PROA enhances accessibility and reduces deployment time, significantly improving assembly efficiency.

Keywords:
deployment planningenhanced randomnesslarge-scale measurement fieldremora optimization algorithmtooling occlusion

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Area of Science:

  • Metrology
  • Optimization Algorithms
  • Industrial Engineering

Background:

  • Traditional large-scale measurement deployment planning relies on Monte Carlo simulations, facing high complexity and inefficiency.
  • Existing station planning methods struggle with overall accessibility due to tooling occlusion.

Purpose of the Study:

  • To develop an optimized deployment strategy for large-scale measurement fields.
  • To enhance the remora optimization algorithm (ROA) for improved performance in deployment planning.

Main Methods:

  • Introduced Poisson-like and enhanced randomness strategies to create the improved remora optimization algorithm (PROA).
  • Validated PROA's convergence speed and robustness using CEC benchmark functions.
  • Developed a deployment model to maximize the visible area of measurement targets.
  • Utilized PROA to optimize station deployment planning.

Main Results:

  • PROA demonstrated improved convergence speed (67.5-74%) and robustness (66.67-75%) compared to ROA.
  • PROA achieved a maximum visible area of 83.02% with six stations, an 18.07% improvement over ROA.
  • The PROA-based model significantly reduced deployment time and enabled calculation of overall accessibility.

Conclusions:

  • The PROA offers a more efficient and effective solution for large-scale measurement deployment planning.
  • This approach enhances assembly efficiency in large-size measurement environments by optimizing station placement and accessibility.