Research on fire rescue path optimization of unmanned equipment based on improved Slime mould Algorithm
Haotian Yang, Enliang Wang, Yue Cai, Zhixin Sun · 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2022
An improved slime Mould Algorithm is proposed to solve the problem of optimal path planning for unmanned equipment in fire rescue. By introducing mutation mechanism and dynamic weight coefficient, the problems of slow convergence and low optimization precision of standard SMA algorithm are solved, and the reinforcement learning method is introduced to traverse the whole situation and search for the next optimal solution, in each iteration, slime mould algorithm is used for a local optimization to improve the quality of the solution. The simulation results show that the improved SMA algorithm has high precision and fast convergence speed, which is better than the genetic algorithm and another heuristic under the same conditions At the same time, the improved SMA algorithm is applied to the optimization of fire rescue route, which can effectively plan the rescue route and improve the rescue efficiency.