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An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning.

Yun Ou1, Pengfei Yin2, Liping Mo3

  • 1School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China.

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|February 22, 2023
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Summary
This summary is machine-generated.

This study introduces a hybrid grey wolf optimizer with clone selection algorithm (pGWO-CSA) to improve convergence speed and accuracy. The enhanced algorithm overcomes local optima in complex optimization problems.

Keywords:
clonal selection algorithmgrey wolf optimizernonlinear functionposition-updating strategyrobot path planning

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Standard Grey Wolf Optimizer (GWO) suffers from slow convergence and local optima.
  • Complex optimization tasks require algorithms with enhanced exploration and exploitation capabilities.

Purpose of the Study:

  • To develop a hybrid grey wolf optimizer with clone selection algorithm (pGWO-CSA).
  • To enhance convergence speed, accuracy, and the ability to escape local optima.

Main Methods:

  • Modified GWO incorporating a nonlinear convergence factor adjustment.
  • Introduction of specialized alpha and beta wolf roles in position updating.
  • Integration of cloning and super-mutation from Clonal Selection Algorithm (CSA).
  • Performance evaluation using 15 benchmark functions and robot path-planning problems.

Main Results:

  • pGWO-CSA demonstrated superior performance compared to GWO and its variants on benchmark functions.
  • Statistical analysis confirmed the algorithm's effectiveness.
  • Successful application to robot path-planning problems, yielding excellent results.

Conclusions:

  • The proposed pGWO-CSA effectively addresses limitations of the standard GWO.
  • The hybrid approach significantly improves optimization performance for complex problems.
  • pGWO-CSA shows strong potential for real-world applications like robot path planning.