Multi-Strategy Improved Escape Optimization Algorithm for Mobile Robot Path Planning in the Complex Environment

To solve the mobile robot path planning in the complex environment, a multi-strategy improved escape optimization algorithm (MESC) is proposed in this paper. In view of the low search efficiency of the original ESC algorithm, it is easy to fall into the problem of local optimal. MESC algorithm introduces the good point set initialization, nonlinear adaptive incremental inertia weight factor and genetic algorithm's variation strategy in the initialization stage, position update stage and optimal solution output stage respectively. The superior performance of MESC algorithm is verified on 8 test functions. Compared with other seven advanced comparison algorithms, MESC algorithm has great advantages in solving accuracy and searching efficiency. At the same time, for the path planning problem in complex environment, the proposed MESC algorithm can effectively obtain the global optimal and smoother path in both simulation and experiment, and the related performance is better than that of ESC algorithm, which verifies the excellent performance of MESC algorithm in path planning problem and the effectiveness of improvement strategy.