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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material

Song Han1, Shanshan Chen1, Fengting Yan1

  • 1School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
Summary
This summary is machine-generated.

The hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE) enhances optimization by combining population evolution with equilibrium optimization. This novel approach improves convergence speed and accuracy for complex problems like AGV scheduling.

Keywords:
equilibrium optimization algorithmgrouping and parallelismhybrid methodphasmatodea population evolution algorithmworkshop material scheduling

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Area of Science:

  • Meta-heuristic algorithms
  • Computational intelligence
  • Evolutionary computation

Background:

  • The phasmatodea population evolution algorithm (PPE) simulates stick insect evolution but suffers from slow convergence and local optima.
  • Existing meta-heuristics require improvements in efficiency and global search capabilities.

Purpose of the Study:

  • To develop an enhanced optimization algorithm that overcomes the limitations of the original PPE.
  • To improve convergence speed, accuracy, and the ability to escape local optima.

Main Methods:

  • Hybridization of the PPE with the equilibrium optimization algorithm.
  • Implementation of parallel processing for population grouping.
  • Proposal of the hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE).

Main Results:

  • HP_PPE demonstrated superior performance compared to similar algorithms on the CEC2017 benchmark functions.
  • The algorithm achieved better convergence speed and accuracy.
  • HP_PPE successfully solved the AGV workshop material scheduling problem with improved results.

Conclusions:

  • The proposed HP_PPE algorithm effectively addresses the limitations of the original PPE.
  • Hybridization and parallel processing significantly enhance optimization performance.
  • HP_PPE offers a promising solution for complex scheduling and optimization tasks.