Multi-strategy Fusion Differential Evolution Algorithm for UAV Path Planning in Complex Environment

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment.