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Updated: May 29, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Improved snow geese algorithm for engineering applications and clustering optimization.

Haihong Bian1,2, Can Li3,4, Yuhan Liu1,2

  • 1College of Electrical Engineering, Nanjing Institute of Technology, Chunhua Street, Nanjing, 211167, Jiangsu, China.

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Summary

The improved Snow Goose Algorithm (ISGA) enhances optimization by addressing local optima and premature convergence. This novel approach demonstrates faster speeds and superior solutions for complex problems.

Keywords:
Engineering and clustering optimizationHonk-guiding mechanismLead goose rotation mechanismMeta-heuristic algorithmOutlier boundarySnow geese algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Meta-heuristics

Background:

  • The Snow Goose Algorithm (SGA), proposed in 2024, shows promise but suffers from local optima and premature convergence.
  • Enhancing meta-heuristic algorithms is crucial for improving their efficiency and effectiveness in complex optimization tasks.

Purpose of the Study:

  • To improve the optimization performance of the Snow Goose Algorithm (SGA).
  • To enhance the exploration and development capabilities, convergence accuracy, and speed of the SGA.

Main Methods:

  • Proposing an improved Snow Goose Algorithm (ISGA) incorporating three novel strategies inspired by snow goose migration: lead goose rotation, honk-guiding, and outlier boundary.
  • Validating ISGA performance on IEEE CEC2022 and IEEE CEC2017 benchmark test suites.
  • Applying ISGA to eight engineering problems and a clustering algorithm to assess practical applicability.

Main Results:

  • ISGA demonstrated superior performance compared to existing algorithms, exhibiting faster iteration speeds.
  • The improved algorithm consistently found better solutions across benchmark test sets and engineering problems.
  • ISGA effectively enhanced the performance of the clustering algorithm.

Conclusions:

  • The proposed ISGA effectively overcomes the limitations of the original SGA, particularly local optima and premature convergence.
  • ISGA shows significant potential for solving complex, real-world optimization problems due to its enhanced speed and solution quality.