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Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning.

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|September 27, 2024
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Summary
This summary is machine-generated.

A new Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm enhances robot path planning. MIHHO improves solution accuracy and convergence speed, outperforming the standard Harris Hawk Optimization (HHO).

Keywords:
Dimension Learning-Based Hunting search strategyDung Beetle Optimizer algorithmHarris Hawk Optimization algorithmdouble adaptive weight strategypath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Path planning is crucial for autonomous mobile robot navigation.
  • Standard Harris Hawk Optimization (HHO) suffers from low accuracy, slow convergence, and local optima issues in path planning.

Purpose of the Study:

  • To propose a novel Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm for enhanced robot path planning.
  • To address the limitations of HHO in terms of solution accuracy, convergence speed, and avoiding local optimization.

Main Methods:

  • Implemented a double adaptive weight strategy to boost search capabilities and improve convergence.
  • Introduced a Dimension Learning-based Hunting (DLH) strategy to balance exploration/exploitation and maintain population diversity.
  • Incorporated a Dung Beetle Optimizer-based position update strategy to mitigate local optima entrapment.

Main Results:

  • MIHHO demonstrated superior performance on test functions, showing significant improvements in optimization ability, convergence speed, and stability.
  • Applied to robot path planning, MIHHO achieved average path length reductions of 1.99% to 14.45% across four different environments compared to HHO.

Conclusions:

  • The MIHHO algorithm offers significant advantages for mobile robot path planning.
  • MIHHO effectively enhances path planning efficiency and accuracy, overcoming limitations of traditional HHO.