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An Enhanced Tree-Seed Algorithm for Function Optimization and Production Optimization.

Qingan Zhou1, Rong Dai2, Guoxiao Zhou3

  • 1School of Geoscience, Yangtze University, Wuhan 430100, China.

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|June 26, 2024
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Summary
This summary is machine-generated.

This study enhances the tree-seed algorithm (TSA) with water-cycling and quantum mechanisms. The improved TSA effectively escapes local optima for better global optimization in complex engineering and energy production challenges.

Keywords:
evolutionary algorithmglobal optimizationindustrial productionreservoir productiontree-seed algorithm

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Area of Science:

  • Engineering
  • Energy Systems
  • Geological Modeling

Background:

  • Increasing complexity in engineering, energy, and geology demands advanced optimization techniques.
  • Evolutionary algorithms offer gradient-free solutions for complex problems.
  • The standard tree-seed algorithm (TSA) can suffer from premature convergence to local optima.

Purpose of the Study:

  • To improve the tree-seed algorithm (TSA) by addressing its limitations in exploration and exploitation balance.
  • To enhance the TSA's ability to find global optima and avoid local peaks.
  • To validate the improved TSA's performance in benchmark tests and real-world applications.

Main Methods:

  • Incorporation of water-cycling and quantum rotation-gate mechanisms into the TSA.
  • Comparative analysis using the CEC 2017 benchmark test suite.
  • Evaluation of the algorithm's performance on reservoir production optimization problems.

Main Results:

  • The enhanced TSA demonstrates a faster convergence rate compared to the standard TSA and other metaheuristic algorithms.
  • The improved algorithm shows a superior ability to locate global optima.
  • The optimized reservoir production models using the enhanced TSA yield better results than competing methods.

Conclusions:

  • The proposed enhancements effectively balance the exploitation and exploration phases of the TSA.
  • The improved TSA offers a robust and efficient solution for complex optimization tasks in engineering and energy sectors.
  • The algorithm shows significant potential for practical applications in real-world production optimization.