scholarly journals Reentry Trajectory Optimization for a Hypersonic Vehicle Based on an Improved Adaptive Fireworks Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xing Wei ◽  
Lei Liu ◽  
Yongji Wang ◽  
Ye Yang
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problems ◽  
Polynomial Interpolation ◽  
Hypersonic Vehicle ◽  
Local Optimum ◽  
New Mutation ◽  
Fireworks Algorithm ◽  
Mutation Strategy ◽  
Path Constraints ◽  
Reentry Trajectory

Generation of optimal reentry trajectory for a hypersonic vehicle (HV) satisfying both boundary conditions and path constraints is a challenging task. As a relatively new swarm intelligent algorithm, an adaptive fireworks algorithm (AFWA) has exhibited promising performance on some optimization problems. However, with respect to the optimal reentry trajectory generation under constraints, the AFWA may fall into local optimum, since the individuals including fireworks and sparks are not well informed by the whole swarm. In this paper, we propose an improved AFWA to generate the optimal reentry trajectory under constraints. First, via the Chebyshev polynomial interpolation, the trajectory optimization problem with infinite dimensions is transformed to a nonlinear programming problem (NLP) with finite dimension, and the scope of angle of attack (AOA) is obtained by path constraints to reduce the difficulty of the optimization. To solve the problem, an improved AFWA with a new mutation strategy is developed, where the fireworks can learn from more individuals by the new mutation operator. This strategy significantly enhances the interactions between the fireworks and sparks and thus increases the diversity of population and improves the global search capability. Besides, a constraint-handling technique based on an adaptive penalty function and distance measure is developed to deal with multiple constraints. The numerical simulations of two reentry scenarios for HV demonstrate the validity and effectiveness of the proposed improved AFWA optimization method, when compared with other optimization methods.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

scholarly journals Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehe Zhu ◽  
Hua Wang ◽  
Jin Zhang
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Local Optimum ◽  
Local Optima ◽  
Trial Vector ◽  
Mutation Strategies

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.

Trajectory Optimization for Hypersonic Vehicle Satisfying Maneuvering Penetration

2011 ◽  
Vol 110-116 ◽  
pp. 5223-5231 ◽  
Author(s):  
Ke Nan Zhang ◽  
Wan Chun Chen
Keyword(s):  
Trajectory Optimization ◽  
Shooting Method ◽  
Control Variable ◽  
Optimization Method ◽  
Hypersonic Vehicle ◽  
Quadratic Program ◽  
Sqp Method ◽  
Piecewise Constant ◽  
Path Constraints ◽  
Direct Shooting

A trajectory optimization method for hypersonic vehicle in glide phase satisfying maneuvering penetration is proposed. Divide the dangerous zones that the hypersonic vehicle may encounter during glide phase into avoidable no-fly zones and avoidless no-fly zones. Take the avoidable no-fly zones as path constraints to join the trajectory optimization. To penetrate the avoidless no-fly zones, trajectory is programmed by some maneuvering policy. Direct shooting method is used to discretize the control variable to piecewise constant functions. So the optimal control problem is transferred to a nonlinear programming (NLP) problem, and solved by the serial quadratic program (SQP) method.

scholarly journals A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Chiwen Qu ◽  
Zhiliu Zeng ◽  
Jun Dai ◽  
Zhongjun Yi ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Convergence Speed ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Search Range ◽  
Inertia Weight ◽  
Mutation Strategy ◽  
Sine Cosine Algorithm ◽  
Conversion Parameter

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.

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