Abstract
The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.
Highlights
Published: 5 March 2021With the continuous development of unmanned aerial vehicle (UAV), the use of this technology in civilian commercial and military fields is increasing
equilibrium optimizer (EO) relies on the particles in the equilibrium pool to generate candidate particles, which still suffers from the defects of reduced population diversity and falling into the local optimum
In order to overcome the shortcomings of basic EO, this paper proposes a multiple population hybrid equilibrium optimizer (MHEO)
Summary
With the continuous development of UAVs, the use of this technology in civilian commercial and military fields is increasing. A series of intelligent optimization algorithms have been used to solve the UAV path planning problem thanks to continuous research in its area. Qu et al [14] combine a simplified grey wolf optimizer with an improved symbiotic biological search and applies it to solving the UAV path planning problem. Yu et al [15] present a differential evolutionary algorithm with adaptive selection variance constraints and solve the UAV path planning problem in disaster environments. Propose a solar-powered UAV path planning framework for complex urban environments and solve it using an improved whale optimization algorithm that includes an adaptive switching strategy and a coordinated decision mechanism. Vincent et al [20] propose a genetic algorithm implemented in parallel on a graphics processing unit and apply it to the UAV path planning problem-solving.
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