Chicken Swarm–Based Method for Ascent Trajectory Optimization of Hypersonic Vehicles

Li, Yongtao; Wu, Yu; Qu, Xiangju

doi:10.1061/(asce)as.1943-5525.0000757

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…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].…”

Section: Optimization On Engineering Problemsmentioning

confidence: 99%

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.

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Section: Optimization On Engineering Problemsmentioning

confidence: 99%

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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“…In order to verify the optimization precision and convergence speed of the algorithm, 30 standard test functions with constraints [36]- [38] 2,3,4,6,9,10,11,12,13,14,15,16,17,18,19,20,22), ε i = 10 −8 (i = 21, 23,24,25,26,27,28,29,30),ε i = 10 −1 (i = 5, 7, 8); other parameter settings are shown in TABLE 3.…”

Section: Test Functionsmentioning

confidence: 99%

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

et al. 2019

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Aiming at the problem of slow convergence speed and ease of falling into local optimum when solving high dimensional problems, this paper proposes an improved chicken swarm optimization algorithm. The improved chicken swarm optimization includes four aspects, namely, cock position update mode, hen position update mode, chick position update mode, and population update strategy, so it is abbreviated as ICSO-RHC. On the basis of algorithm improvement, the influence of the number of retained elite individuals and control parameters on the convergence speed of the algorithm is discussed. The calculation results of the test function show that when the number of elite individuals in the population is 1, and the control parameters is a random number uniformly distributed between [0, 1], the algorithm has a faster convergence speed. In addition, in order to verify the performance of ICSO-RHC, 30 test functions and CEC 2005 benchmark functions were selected. The calculation results of these test functions show that the success rate of ICSO-RHC is significantly higher than other algorithms, both for low-dimensional and highdimensional optimization problems. The average iteration number and average running time are significantly lower than other algorithms. Finally, ICSO-RHC and other improved algorithms in the literature are used to optimize the parameters of four practical engineering problems. The optimization results show that the statistical results obtained by ICSO-RHC are significantly better than other algorithms. The calculation results of the test functions and the actual engineering problems show that the performance of ICSO-RHC proposed in this paper is significantly better than other algorithms.INDEX TERMS Constrained optimization problem, chicken swarm optimization, rooster position update, hen position update, chick position update.

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“…The CSO algorithm is a more general form of the PSO algorithm and has shown its advantages in function optimization problem 23 and nonlinear trajectory optimization problem. 24 To deal with the nonlinear model of aircraft and multiple constraints in the problem, CSO algorithm is applied in this study.…”

Section: The Ground Motion Of Aircraft Is Ignored: Aircraftmentioning

confidence: 99%

A general trajectory optimization method for aircraft taxiing on flight deck of carrier

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Enhancing operation efficiency of flight deck has become a hotspot because it has an important impact on the fighting capacity of the carrier–aircraft system. To improve the operation efficiency, aircraft need taxi to the destination on deck with the optimal trajectory. In this paper, a general method is proposed to solve the trajectory optimization problem for aircraft taxiing on flight deck considering that the existing methods can only deal with the problem in some specific cases. Firstly, the ground motion model of aircraft, the collision detection strategy and the constraints are included in the mathematical model. Then the principles of the chicken swarm optimization algorithm and the generality of the proposed method are explained. In the trajectory optimization algorithm, several strategies, i.e. generation of collocation points, transformation of control variable, and setting of segmented fitness function, are developed to meet the terminal constraints easier and make the search efficient. Three groups of experiments with different environments are conducted. Aircraft with different initial states can reach the targets with the minimum taxiing time, and the taxiing trajectories meet all the constraints. The reason why the general trajectory optimization method is validated in all kinds of situations is also explained.

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Chicken Swarm–Based Method for Ascent Trajectory Optimization of Hypersonic Vehicles

Cited by 30 publications

References 26 publications

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

A general trajectory optimization method for aircraft taxiing on flight deck of carrier

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