Improved Chicken Swarm Optimization Method for Reentry Trajectory Optimization

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.

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

“…In the cloud computing domain, CSO has been used to solve deadlock-free migration of the virtual machine (VM) [28] and dynamic task scheduling problem [29]. In the military field, CSO is applied to optimize the warhead's reentry trajectory [30] and ascent trajectory of hypersonic vehicles [31]. In the data mining domain, it is used to optimize the K-means clustering algorithm [32], adaptive neurofuzzy inference system [33], and feature selection problem [34].…”

Improved Dynamical Particle Swarm Optimization Method for Structural Dynamics

“…(c) To preserve an adequate diversity during the searching process: an exploration dynamic operator and a kind of elitist crossover operator inspired by the ideas discussed by Wu et al [29] have been implemented in this work. The authors developed an Improved Chicken Swarm Optimization Method applied to calculate reentry trajectories.…”

An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers

“…In the field of control, the CSO algorithm has also proven to be a good algorithm. For example, in [26], Li et al showed a chicken swarm-based method for ascent trajectory optimization of hypersonic vehicles; in [27], Amador-Angulo et al presented an optimal design of fuzzy logic systems through a Chicken Search Optimization Algorithm applied to a benchmark problem; in [28], Liang et al presented an improved Chicken Swarm Optimization Algorithm and its application in robot path planning; in [29], Mu et al presented an optimal trajectory planning for robotic manipulators using chicken swarm optimization; and in [30], Wu et al presented an improved Chicken Swarm Optimization method for reentry trajectory optimization.…”