Ascent Trajectory Optimization for Hypersonic Vehicle Based on Improved Chicken Swarm Optimization

Fu, Wenzhe; Wang, Bo; Li, Xu; Liu, Lei; Wang, Yongji

doi:10.1109/access.2019.2947297

Cited by 20 publications

(17 citation statements)

References 37 publications

(49 reference statements)

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“…Only it is necessary to continuously adjust the algorithm parameters according to the optimization results to obtain the purpose needed. Another advantage of treating the problem as a black box is flexibility, which means that stochastic algorithms can be conveniently applied to solve various engineering difficulties in different fields [3], [4].…”

Section: Introductionmentioning

confidence: 99%

A Modified Sine Cosine Algorithm for Solving Optimization Problems

Wang

2021

IEEE Access

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The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions. INDEX TERMS Sine cosine algorithm, linear searching path, nature-inspired algorithm, optimization.

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Section: Introductionmentioning

confidence: 99%

A Modified Sine Cosine Algorithm for Solving Optimization Problems

Wang

2021

IEEE Access

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“…Torabi and Safi-Esfahani [13] combined CSO with an improved raven roosting optimization (RRO) algorithm in order to balance the global and local searching capabilities. Fu et al [14] modified the update equation of roosters and introduced a mutation operator to solve the problem of easily falling into a trap of local optimal solutions. Furthermore, the CSO algorithm has also been extended to solve the constraint optimization problem [15] (see Section 6.1), 0-1 knapsack problem [16], and multiobjective optimization problem [17].…”

Section: Introductionmentioning

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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“…Trajectory optimization of the launch vehicle is in general considered a typical nonlinear optimal control problem [1]. Initially, analytical methods are developed to solve ascent trajectory optimization problems based on the optimal control theory [2][3][4], which usually appears in the indirect methods [5][6][7]. In this kind of method, the optimization problem is first converted by formulating a Hamilton function and then solved by solving a two-point boundary value problem using the maximum principle [8].…”

Section: Introductionmentioning

confidence: 99%

Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

Lin

Zhang

Hong-yu

et al. 2021

International Journal of Aerospace Engineering

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This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.

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Ascent Trajectory Optimization for Hypersonic Vehicle Based on Improved Chicken Swarm Optimization

Cited by 20 publications

References 37 publications

A Modified Sine Cosine Algorithm for Solving Optimization Problems

A Modified Sine Cosine Algorithm for Solving Optimization Problems

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

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