Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

[1]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[2]  Chih-Min Lin,et al.  Intelligent control system design for UAV using a recurrent wavelet neural network , 2012, Neural Computing and Applications.

[3]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

[4]  Gang Wang,et al.  Multiple parameter control for ant colony optimization applied to feature selection problem , 2015, Neural Computing and Applications.

[5]  Jun Zhou,et al.  Terminal area guidance for reusable launch vehicles , 2016 .

[6]  Honglun Wang,et al.  A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization , 2015, Neurocomputing.

[7]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[8]  Surapong Auwatanamongkol,et al.  A crowding multi-objective genetic algorithm for image parsing , 2015, Neural Computing and Applications.

[9]  Diego Oliva,et al.  Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm , 2017 .

[10]  K. Subbarao,et al.  Hybrid Genetic Algorithm Collocation Method for Trajectory Optimization , 2009 .

[11]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[12]  Yufei Zhuang,et al.  Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm , 2014 .

[13]  G Morani,et al.  On-line guidance with trajectory constraints for terminal area energy management of re-entry vehicles , 2011 .

[14]  Kenneth D. Mease,et al.  Reduced-Order Entry Trajectory Planning for Acceleration Guidance , 2002 .

[15]  V. K. Jayaraman,et al.  Ant Colony Approach to Continuous Function Optimization , 2000 .

[16]  Meng Joo Er,et al.  Rectilinear workspace partitioning for parallel coverage using multiple unmanned aerial vehicles , 2007, Adv. Robotics.

[17]  Adem Alpaslan Altun,et al.  Cost optimization of mixed feeds with the particle swarm optimization method , 2011, Neural Computing and Applications.

[18]  Behnam Mohammadi-Ivatloo,et al.  Combined heat and power economic dispatch problem solution by implementation of whale optimization method , 2017, Neural Computing and Applications.

[19]  P. M. Suárez,et al.  Initial guess of the solution of dynamic optimization of chemical processes , 2010 .

[20]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2005, SIAM Rev..

[21]  Aboul Ella Hassanien,et al.  Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation , 2017, Expert Syst. Appl..

[22]  Honglun Wang,et al.  A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints , 2016, Neurocomputing.

[23]  Anil V. Rao,et al.  Algorithm 902: GPOPS, A MATLAB software for solving multiple-phase optimal control problems using the gauss pseudospectral method , 2010, TOMS.

[24]  William W. Hager,et al.  Pseudospectral methods for solving infinite-horizon optimal control problems , 2011, Autom..

[25]  MirjaliliSeyedali,et al.  Grasshopper Optimisation Algorithm , 2017 .

[26]  Xiuyun Meng,et al.  Re-entry trajectory optimization using an hp-adaptive Radau pseudospectral method , 2013 .

[27]  Tommy W. S. Chow,et al.  Object-Level Video Advertising: An Optimization Framework , 2017, IEEE Transactions on Industrial Informatics.

[28]  Hossam Faris,et al.  Optimizing connection weights in neural networks using the whale optimization algorithm , 2016, Soft Computing.

[29]  Yu Xie,et al.  Highly constrained entry trajectory generation , 2013 .

[30]  Bijaya K. Panigrahi,et al.  A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning , 2016, Swarm Evol. Comput..

[31]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[32]  AMIT AGARWAL,et al.  Rectilinear Workspace Partitioning for Parallel Coverage Using Multiple UAVs , 2006 .

[33]  J. Betts.  Survey of Numerical Methods for Trajectory Optimization , 1998 .

[34]  Xuelian Jin,et al.  Gauss Pseudospectral Method Applied to Multi-Objective Spacecraft Trajectory Optimization , 2014 .

[35]  Hamidreza Modares,et al.  Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm , 2011, Eng. Appl. Artif. Intell..

[36]  Rui Zhou,et al.  Reentry trajectory optimization for hypersonic vehicle satisfying complex constraints , 2013 .

[37]  Honglun Wang,et al.  Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer , 2017 .

[38]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[39]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[40]  Jaime Llorca,et al.  Nature-Inspired Self-Organization, Control, and Optimization in Heterogeneous Wireless Networks , 2012, IEEE Transactions on Mobile Computing.

[41]  Rui Zhou,et al.  Particle swarm optimization applied to hypersonic reentry trajectories , 2015 .

[42]  Ah-Hwee Tan,et al.  Extreme learning machine terrain-based navigation for unmanned aerial vehicles , 2012, Neural Computing and Applications.

[43]  Geoffrey T. Huntington,et al.  Comparison of Global and Local Collocation Methods for Optimal Control , 2008 .

[44]  Anil V. Rao,et al.  Practical Methods for Optimal Control Using Nonlinear Programming , 1987 .