Yan Chen
ABSTRACT
Based on various constraints of actual parking problems, this paper constructs an automatic parking optimization model by taking the shortest parking trajectory as the optimization index and combines with the cubic spline theory. Firstly, a strategy of nonlinear decreasing inertia weight with iterative number is designed to enhance the global convergence ability of particle swarm optimization. Then, combined with genetic evolution mechanism, an adaptive mutation strategy is introduced to enhance the particle swarm diversity maintenance ability, so as to effectively improve its global convergence ability and avoid premature convergence in the late iteration. The simulation results of the test function and the actual problem of automatic parking for entering parking space indicate that the improved algorithm has higher searching accuracy and faster convergence speed.
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Index Terms
- Improved Particle Swarm Optimization Algorithm for Automatic Entering Parking Space Based on Spline Theory
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