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Improved Immune Moth-Flame Algorithm for Intelligent Vehicle Parking Path Optimization.

Yan Chen1, Longda Wang2, Xiujiang Zhu2

  • 1School of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, China.

Biomimetics (Basel, Switzerland)
|April 27, 2026
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Summary
This summary is machine-generated.

This study introduces an improved immune moth-flame optimization algorithm (IIMFO) for intelligent vehicle parking path planning. The enhanced algorithm achieves smoother and more optimal parking trajectories, addressing current limitations in intelligent parking systems.

Keywords:
cubic spline interpolationimmune mechanismintelligent trajectory optimizationmoth–flame algorithmparking trajectory optimization

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Area of Science:

  • Robotics and Artificial Intelligence
  • Automotive Engineering
  • Optimization Algorithms

Background:

  • Intelligent parking systems are crucial for addressing parking shortages and enhancing traffic safety.
  • Conventional path optimization algorithms struggle to generate smooth and accurate parking trajectories.
  • Complex automatic parking trajectory optimization remains a significant challenge.

Purpose of the Study:

  • To develop an improved optimization algorithm for intelligent vehicle parking path planning.
  • To achieve shortest path length and smooth trajectories in automatic parking.
  • To enhance the performance of existing optimization models for intelligent parking.

Main Methods:

  • Development of an improved immune moth-flame optimization algorithm (IIMFO).
  • Leveraging a cubic spline interpolation-driven path planning model.
  • Incorporation of immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning.

Main Results:

  • The IIMFO algorithm demonstrated superior performance in test functions and simulations.
  • The improved strategy successfully generated smoother and more optimal parking trajectories.
  • Experimental validation in real-world intelligent vehicle parking scenarios confirmed effectiveness.

Conclusions:

  • The developed IIMFO algorithm significantly enhances intelligent vehicle parking path planning.
  • The cubic spline interpolation and enhanced optimization mechanisms lead to better trajectory generation.
  • This approach offers a promising solution for efficient and safe automatic parking.