Abstract
Reentry trajectory optimization has been researched as a popular topic because of its wide applications in both military and civilian use. It is a challenging problem owing to its strong nonlinearity in motion equations and constraints. Besides, it is a high-dimensional optimization problem. In this paper, an improved chicken swarm optimization (ICSO) method is proposed considering that the chicken swarm optimization (CSO) method is easy to fall into local optimum when solving high-dimensional optimization problem. Firstly, the model used in this study is described, including its characteristic, the nonlinear constraints, and cost function. Then, by introducing the crossover operator, the principles and the advantages of the ICSO algorithm are explained. Finally, the ICSO method solving the reentry trajectory optimization problem is proposed. The control variables are discretized at a set of Chebyshev collocation points, and the angle of attack is set to fit with the flight velocity to make the optimization efficient. Based on those operations, the process of ICSO method is depicted. Experiments are conducted using five algorithms under different indexes, and the superiority of the proposed ICSO algorithm is demonstrated. Another group of experiments are carried out to investigate the influence of hen percentage on the algorithm’s performance.
Highlights
In recent years, the study on hypersonic vehicle has a rapid development with human’s exploration in space [1]
The results indicate that the chicken swarm optimization (CSO) algorithm is easy to fall into local optimum, and premature convergence occurs [14]
The improved chicken swarm optimization (ICSO) algorithm has the fastest convergence rate, while the ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms perform the worst. These results demonstrate that the ICSO algorithm is better than the other four algorithms both in minimizing the fitness value and in improving the convergence rate
Summary
The study on hypersonic vehicle has a rapid development with human’s exploration in space [1]. Other swarm intelligence algorithms, such as PSO algorithm [15], ant colony optimization (ACO) algorithm [16], and artificial bee colony (ABC) algorithm [17] have been used to obtain the reentry trajectories for hypersonic vehicle In these studies, the problem is formulated into a high-dimensional optimization problem with strong nonlinearity and multiple constraints, and the control variables of vehicle in different moments are optimized by various swarm intelligence algorithms, respectively. The improved version of CSO is expected to show its superiority in solution quality and convergence rate over other swarm intelligence algorithms To this end, an improved chicken swarm optimization (ICSO) method solving the reentry trajectory optimization problem is proposed for the first time in this paper.
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