A new hybrid algorithm for path planning of mobile robot

The selection of algorithm is the most critical part in the mobile robot path planning. At present, the commonly used algorithms for path planning are genetic algorithm (GA), ant colony algorithm (ACA), and firefly algorithm (FA). Among them, FA is more typical. FA has the disadvantage of being easily trapped into a local optimal solution. In order to improve this disadvantage, this paper proposes a new hybrid algorithm which is based on GA and FA. The core idea of this new algorithm is that when the FA falls into the local optimal solution, the local optimal fireflies would be regarded as a group, and the group is subjected to the selection, crossover and mutation operations in the GA. Finally, the optimal firefly individual can be obtained from genetic operations. Theoretical and experimental results have verified that the new hybrid algorithm can improve the accuracy and performance of the FA. Applying the new hybrid algorithm to path planning can improve the robot’s reaction ability and computing power in path planning.