Gradient eigendecomposition invariance biogeography-based optimization for mobile robot path planning

The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increasingly. In paper, we propose a novel gradient eigendecomposition invariance biogeography-based optimization (GEI-BBO) robot planning, which the merits of high rotation excellent search performance. GEI-BBO, design an mechanism migration operation, can reduce dependency (BBO) on coordinate system, improve share information between eigensolutions more effectively. Meanwhile, find local optimal solution better, descent is added, system strategy occurrence trapping phenomenon. addition, combining GEI-BBO with cubic spline interpolation will solve through defined coding method fitness function. A series experiments are implemented benchmark functions, whose results indicated that performance superior other algorithms. And successful application in different environments confirms its effectiveness practicability.