Junfeng Xin, Jiabao Zhong, Jinlu Sheng, Penghao Li and Ying Cui


  1. [1] W.C. Jackson and J.D. Norgard, A hybrid genetic algorithm with Boltzmann convergence properties. Journal of Optimization Theory and Applications, 136(3), 2007, 431–443.
  2. [2] J. Yang, X. Shi, M. Marchese, and Y. Liang, An ant colony optimization method for generalized TSP problem Progress in Natural Science, 18(11), 2008, 1417–1422.
  3. [3] L.P. Behnck, D. Doering, C.E. Pereira, and A. Rettberg, A modified simulated annealing algorithm for SUAVs path planning, IFAC-PapersOnLine, 48(10), 2015, 63–68.
  4. [4] S. Zhang, Y. Zhou, Z. Li, and W. Pan, Grey wolf optimizer for unmanned combat aerial vehicle path planning, Advances in Engineering Software, 99, 2016, 121–136.
  5. [5] D. Xin, C. Huahua, G. Weikang, et al., Neural network and genetic algorithm based global path planning in a static environment, Journal of Zhejiang University Science, 6(6), 2005, 549–554.
  6. [6] J.H. Holland, Outline for a logic theory of adaptive systems, Journal of the Association for Computing Machinery, 9(3), 1962, 297–314.
  7. [7] J. H. Holland. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88–105, 1973.
  8. [8] H. Kim, S. H. Kim, M. Jeon, J. Kim, S. Song, K. J. Paik. A study on path optimization method of an unmanned surface vehicle under environmental loads using genetic algorithm. Ocean Engineering, 142, 616–624, 2017.
  9. [9] L. Cao, Improved genetic algorithm for fast path planning of USV, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 2015. 721
  10. [10] J. Wang, O.K. Ersoy, M. He, and F. Wang, Multi-offspring genetic algorithm and its application to the traveling salesman problem, Applied Soft Computing, 43, 2016, 415–423.
  11. [11] C.A.D. Silva, ´A.V.F.M. De Oliveira, and M.A.C. Fernandes, Validation of a dynamic planning navigation strategy applied to mobile terrestrial robots, Sensors, 18, 2018, 4322.
  12. [12] C.H. Chen, Y.H. Chen, J.C.W. Lin, and M.E. Wu, An effective approach for obtaining a group trading strategy portfolio using grouping genetic algorithm, IEEE Access, 7, 2019, 7313–7325.
  13. [13] A. Ratnaweera, S.K. Halgamuge, and H.C. Watson, Self- organizing hierarchical particle swarm optimizer with time- varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, 8(3), 2004, 240–255.
  14. [14] A. Tuncer and M. Yildirim, Dynamic path planning of mobile robots with improved genetic algorithm, Computers & Electrical Engineering, 38(6), 2012, 1564–1572.
  15. [15] M. Albayrak and N. Allahverdi, Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms, Expert Systems with Applications, 38(3), 2011, 1313–1320.
  16. [16] J. Xin, J. Zhong, F. Yang, Y. Cui, and J. Sheng, An improved genetic algorithm for path-planning of unmanned surface vehicle, Sensors, 19, 2019, 2640.
  17. [17] J. Xin, S. Li, J. Sheng, Y. Zhang, and Y. Cui, Application of improved particle swarm optimization for navigation of unmanned surface vehicles, Sensors, 19, 2019, 3096.
  18. [18] J. Xin, J. Zhong, S. Li, J. Sheng, and Y. Cui, Greedy mechanism based particle swarm optimization for path planning problem of an unmanned surface vehicle, Sensors, 19, 2019, 4620.

Important Links:

Go Back