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A Multi-Strategy Improved Zebra Optimization Algorithm for AGV Path Planning.

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Biomimetics (Basel, Switzerland)
|October 28, 2025
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The Multi-strategy Improved Zebra Optimization Algorithm (MIZOA) enhances swarm intelligence by improving convergence and exploration. This novel algorithm shows superior performance in optimization and path planning for Automated Guided Vehicles (AGVs).

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AGV path planningcoati optimization algorithmmetropolis criterionmulti-population search strategymutation operationzebra optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence Algorithms
  • Metaheuristic Optimization

Background:

  • The Zebra Optimization Algorithm (ZOA) suffers from slow convergence and local optima issues.
  • Existing swarm intelligence algorithms often struggle with exploration-exploitation balance.

Purpose of the Study:

  • To introduce an improved Zebra Optimization Algorithm (ZOA), named Multi-strategy Improved Zebra Optimization Algorithm (MIZOA).
  • To enhance global convergence, exploration, and exploitation capabilities of the ZOA.

Main Methods:

  • Implemented a multi-population search strategy for increased diversity.
  • Integrated Genetic Algorithm (GA) mutation with Metropolis criterion for adaptive exploration/exploitation.
  • Incorporated Coati Optimization Algorithm (COA) hunting behavior and Lévy flight for enhanced global search.

Main Results:

  • MIZOA demonstrated superior global convergence accuracy and optimization performance on 23 benchmark functions.
  • Statistical tests (Wilcoxon, Friedman) confirmed MIZOA's robustness against eight other algorithms.
  • MIZOA outperformed seven algorithms on real-world engineering problems and AGV path planning.

Conclusions:

  • The proposed MIZOA effectively addresses limitations of the traditional ZOA.
  • MIZOA offers robust and effective optimization, particularly for complex tasks like AGV path planning.