Automatic Parking Path Optimization Based on Immune Moth Flame Algorithm for Intelligent Vehicles

Automatic parking path optimization is a key point for automatic parking. However, it difficult to obtain the smooth, accurate and optimal by using traditional algorithms. So, based on model cubic spline interpolation, an improved immune moth flame algorithm proposed intelligent vehicles. Firstly, enhance global performance, interpolation designed shortest as target. Secondly, (IIMFO) mechanism, Gaussian mutation mechanism opposition-based learning strategy proposed, adaptive decreasing inertia weight coefficient integrated into so that these strategies can improve balance quality between search local development effectively. Finally, results several common test functions show IIMFO in this paper has higher precision. Furthermore, simulation semi-automatic experiment of practical cases vehicles better effect than