Negative Gradient Differential Biogeography-based Optimization for Mobile Robot Path Planning

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.