Enhanced rime optimization algorithm for robot path planning in complex environments
Addressing the problems of the RIME in the mobile robot path planning problem,such as easy to fall into the local optimum and slow convergence speed,this paper proposed an enhanced rime optimization algorithm(ERIME)for the path planning of mobile robots in the complex environment.Firstly,this algorithm designed a lens imaging population selection strategy based on sine chaos mapping to improve the population initialization stage to increase the population diversity,so that the algorithm could be better explored and exploited.Secondly,this algorithm designed a stochastic factor-controlled optimal search strategy and a centroid-guided development mechanism to improve the exploration and exploitation stages of the algo-rithm,so as to enhance the algorithm's ability to escape from the local optimal solutions,better explore the global optimal so-lution,and accelerate the convergence speed of the algorithm.Additionally,this paper proposed a Markov chain model of the ERIME algorithm and proved the global convergence of the algorithm.To verify the effectiveness of ERIME,this paper valida-ted the algorithm using the CEC2017 test set and compared it with other well-known meta-heuristic algorithms.The results show that the algorithm performs well.Finally,this paper applied the ERIME algorithm to the path planning problem of mobile robots in complex environments.The experimental results demonstrate that the proposed algorithm efficiently plans the robot's path and finds a high-quality route.
robotpath planningrime optimization algorithm(RIME)grid mapsine mappingbest value search
万方数据
维普
