In order to overcome the disadvantages of Differential Evolution (DE) algorithm such as low optimization accuracy, slow convergence and poor stability, an Adaptive Differential Evolution algorithm based on Multi-Mutation strategy (ADE-MM) was proposed. Firstly, two disturbance thresholds with learning functions were used in the selection of three mutation strategies to increase the diversity of the population and expand the search scope. Then, according to the successful parameters of the last iteration, the current parameters were adjusted adaptively to improve the search accuracy and speed. Finally, vector particle pool method and central particle method were used to generate new vector particles to further improve the search effect. Tests were performed on 8 functions for 5 comparison algorithms (Random Mutation Differential Evolution (RMDE), Cross-Population Differential Evolution algorithm based on Opposition-based Learning (OLCPDE), Adaptive Differential Evolution with Optional External Archive (JADE), Self-adaptive Differential Evolution (SaDE), Modified Differential Evolution with p-best Crossover (MDE_pBX)), and each example was independently performed 30 times. The ADE-MM algorithm achieves a complete victory in the comparison of mean and variance, 5 independent wins and 3 tie wins are achieved in the 30-dimensional case; 6 independent wins and 2 tie wins are obtained in the 50-dimensional case; in 100-dimensional case, all are won independently. At the same time, in the Wilcoxon rank sum test, winning rate and time-consuming analysis, the ADE-MM algorithm also achieves excellent performance. The results show that ADE-MM algorithm has stronger global search ability, convergence and stability than other five comparison algorithms.

In order to solve the problem of rescuing the maximum number of trapped men in limited time after disaster, the robots were used to take place of rescue workers to rescue the survivors after disaster, and the robots rescue path planning method was studied by considering the situation that the trapped men's life strengths were uncertain. Firstly, considering that each target has life strength and the values of life strengths were different due to different factors, the value of life strength was set as interval number in general. Secondly, taking life strength constraint into account, the rescued worker number was treated as the objective function, which is an interval function related to life strength. Then the modified Particle Swarm Optimization (PSO) algorithm was used to solve the established objective function, the particle's code and decode method and the global best solution update strategy were introduced. Finally, the effectiveness of the proposed method was verified by simulations of different scenarios.