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Related Experiment Video

Updated: Aug 23, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Harris hawks optimization based on global cross-variation and tent mapping.

Lei Chen1, Na Song2, Yunpeng Ma1

  • 1School of Information Engineering, Tianjin University of Commerce, Beichen District, Tianjin, 300134 China.

The Journal of Supercomputing
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

A new Harris hawks optimization (HHO) algorithm, CRTHHO, enhances convergence speed and accuracy by incorporating tent mapping and global cross-variation. This improved meta-heuristic algorithm outperforms the basic HHO and competes with advanced methods.

Keywords:
Crossover mutationGreedy selectionHarris hawks optimizationMeta-heuristic algorithmTent mapping

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Meta-heuristic Computing

Background:

  • The Harris hawks optimization (HHO) algorithm, inspired by hawk predation, faces challenges with slow convergence and local optima.
  • These limitations stem from uniform position updates in the exploration phase and insufficient population diversity in later stages.

Purpose of the Study:

  • To address the limitations of the basic HHO algorithm.
  • To propose an enhanced Harris hawks optimization algorithm with improved convergence speed and accuracy.

Main Methods:

  • Introduced tent mapping in the exploration stage to optimize the random parameter q, accelerating early-stage convergence.
  • Integrated a global crossover mutation operator to enhance the global optimal position in each iteration.
  • Employed a greedy selection strategy to prevent premature convergence to local optima and improve solution accuracy.

Main Results:

  • The proposed CRTHHO algorithm demonstrated superior performance compared to the basic HHO algorithm.
  • CRTHHO showed competitive results against five other advanced meta-heuristic algorithms.
  • Experiments were validated on ten benchmark functions and the CEC2017 test set.

Conclusions:

  • The CRTHHO algorithm effectively improves convergence speed and avoids local optima.
  • The enhancements lead to better overall performance and accuracy in optimization tasks.
  • CRTHHO presents a promising alternative for complex optimization problems.