Multi-Region UAV Path Planning Based on Constrained Multi-Objective Optimization
Drone path planning often establishes mathematical models in the form of constrained multi-objective optimization problems,and these models almost only consider path planning between two points in the three-dimen-sional modeling space;However,there is a lack of research on the path planning problem of unmanned aerial vehicles starting from the starting point,passing through multiple designated work areas,and reaching the endpoint.To remedy this issue,this paper proposes a constrained multi-objective evolutionary algorithm with a geographic information-guided search strategy based on infeasible solution-guided population evolution(DW-LS).Firstly,a new constrained multi-objective UAV path planning problem was built by combining environmental constraints,performance con-straints,and multiple region visit tasks of UAV flight.Secondly,by having the utilization mechanism of the infeasible solution,a geographic information-guided search strategy was designed to optimize the infeasible solution to further assist the population in searching for the optimal evolutionary direction.Finally,DW-LS was compared with three rep-resentative constrained multi-objective evolutionary algorithms,and the experimental results show that the performance of our algorithm is superior in terms of convergence and diversity of the obtained feasible solutions.
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