Application of Improved Moth-Flame Optimization Algorithm for Robot Path Planning

Abstract

Path planning is the focus and difficulty of research in the field of mobile robots, and it is the basis for further research and applications of robots. In order to obtain the global optimal path of the mobile robot, an improved moth-flame optimization (IMFO) algorithm is proposed in this paper. The IMFO features the following two improvement. Firstly, referring to the spotted hyena optimization (SHO) algorithm, the concept of historical best flame average is introduced to improve the moth-flame optimization (MFO) algorithm update law to increase the ability of the algorithm to jump out of the local optimum; Secondly, the quasi-opposition-based learning (QOBL) is used to perturb the location, increase the population diversity and improve the convergence rate of the algorithm. In order to evaluate the performance of the proposed algorithm, the IMFO algorithm is compared with three existing algorithms on three groups of different types of benchmark functions. The comparative results show that the IMFO algorithm is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Finally, the IMFO algorithm is applied to the path planning of the mobile robot, and computer simulations confirmed the algorithm’s effectiveness.

Keywords

• Moth-flame optimization
• quasi-opposition-based learning
• spotted hyena optimization
• path planning