A NOVEL ROBOT PATH PLANNING ALGORITHM BASED ON THE IMPROVED WILD HORSE OPTIMISER WITH HYBRID STRATEGIES

Juntao Zhao, Xiaochuan Luo, and Yong Li

References

  1. [1] N.A. Houacine and H. Drias, When robots contribute toeradicate the COVID-19 spread in a context of containment,Progress in Artificial Intelligence, 10(4), 2021, 391–416.
  2. [2] Z. Liu, D. Zhu, C. Liu, and S.X. Yang, A novel pathplanning algorithm of AUV with model predictive control,International Journal of Robotics and Automation, 37(6), 2022,460–467.
  3. [3] C. Ntakolia, S. Moustakidis, and A. Siouras, Autonomous pathplanning with obstacle avoidance for smart assistive systems,Expert Systems with Applications, 213, 2023, 119049.
  4. [4] L. Sun, Z. Fu, F. Tao, P. Si, S. Song, and C. Sun, APF-bug-based intelligent path planning for autonomous vehicle withhigh precision in complex environment, International Journalof Robotics and Automation, 38(6), 2023, 277–283.
  5. [5] M.N.A. Wahab, S. Nefti-Meziani, and A. Atyabi, A comparativereview on mobile robot path planning: Classical or meta-heuristic methods? Annual Reviews in Control, 50, 2020,233–252.
  6. [6] L. Xu, M. Cao, and B. Song, A new approach to smooth pathplanning of mobile robot based on quartic Bezier transitioncurve and improved PSO algorithm, Neurocomputing, 473,2022, 98–106.
  7. [7] S. Kumar and A. Sikander, Optimum mobile robot pathplanning using improved artificial bee colony algorithm andevolutionary programming, Arabian Journal for Science andEngineering, 47(3), 2022, 3519–3539.
  8. [8] M. Alweshah, M. Almiani, N. Almansour, S. Al Khalaileh, H.Aldabbas, W. Alomoush, and A. Alshareef, Vehicle routingproblems based on Harris Hawks optimization, Journal of BigData, 9(1), 2022, 42.
  9. [9] G. Zhang and E. Zhang, An improved sparrow search basedintelligent navigational algorithm for local path planning ofmobile robot, Journal of Ambient Intelligence and HumanizedComputing, 14, 2023, 14111–14123.
  10. [10] I. Naruei and F. Keynia, Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization prob-lems, Engineering with Computers, 38(4), 2022, 3025–3056.
  11. [11] R. Zheng, A.G. Hussien, H.-M. Jia, L. Abualigah, S. Wang,and D. Wu, An improved wild horse optimizer for solvingoptimization problems, Mathematics, 10(8), 2022, 1311.
  12. [12] E. Garc´ıa, J.R. Villar, Q. Tan, J. Sedano, and C. Chira,An efficient multi-robot path planning solution using Aand coevolutionary algorithms, Integrated Computer-AidedEngineering, 30(1), 2023, 41–52.
  13. [13] T. Liu, J. Li, S.X. Yang, Z. Gong, Z, Liu, H. Zhong, and Q.Fu, Optimal coverage path planning for tractors in hilly areasbased on energy consumption model, International Journal ofRobotics and Automation, 38(6), 2023, 20–31.
  14. [14] L. Wu, X. Huang, J. Cui, C. Liu, and W. Xiao, Modifiedadaptive ant colony optimization algorithm and its applicationfor solving path planning of mobile robot, Expert Systems withApplications, 215, 2023, 119410.
  15. [15] W. Hou, Z. Xiong, C. Wang, and H. Chen, Enhanced antcolony algorithm with communication mechanism for mobilerobot path planning, Robotics and Autonomous Systems, 148,2022, 103949.
  16. [16] L. Wang and Y. Guo, Speed adaptive robot trajectorygeneration based on derivative property of B-Spline curve,IEEE Robotics and Automation Letters, 8(4), 2023, 1905–1911.
  17. [17] J. Zhao and X. Luo, Three-dimensional path planning forunmanned aerial vehicle (UAV) based on improved mayflyalgorithm, Proc. 2022 IEEE International Conf. on UnmannedSystems (ICUS). IEEE, Guangzhou, China, 2022, 32–37.
  18. [18] S. Joe and F.Y. Kuo, Remark on algorithm 659: ImplementingSobol’s quasirandom sequence generator, ACM Transactionson Mathematical Software, 29(1), 2003, 49–57.
  19. [19] A.H. Gandomi, X.-S. Yang, and A.H. Alavi, Cuckoo searchalgorithm: A metaheuristic approach to solve structuraloptimization problems, Engineering with Computers, 29(1),2013, 17–35.
  20. [20] Q. Bo, W. Cheng, and M. Khishe, Evolving chimp optimizationalgorithm by weighted opposition-based technique and greedysearch for multimodal engineering problems, Applied SoftComputing, 132, 2023, 109869.
  21. [21] Y. Shen, C. Zhang, F. Soleimanian Gharehchopogh, and S.Mirjalili, An improved whale optimization algorithm basedon multi-population evolution for global optimization andengineering design problems, Expert Systems with Applications,215, 2023, 119269.
  22. [22] S. Zhao, T. Zhang, S. Ma, and M. Wang, Sea-horse optimizer:A novel nature-inspired meta-heuristic for global optimizationproblems, Applied Intelligence, 53(10), 2023, 11833–11860.
  23. [23] G. Hu, J. Wang, Y. Li, M. Yang, and J. Zheng, An enhancedhybrid seagull optimization algorithm with its application inengineering optimization, Engineering with Computers, 39(2),2023, 1653–1696.
  24. [24] M. Abdel-Basset, R. Mohamed, K.M. Sallam, and R.K.Chakrabortty, Light spectrum optimizer: A novel physics-inspired metaheuristic optimization algorithm, Mathematics,10(19), 2022, 3466.
  25. [25] G. Saravanan, S. Neelakandan, P. Ezhumalai, and S. Maurya,Improved wild horse optimization with L´evy flight algorithmfor effective task scheduling in cloud computing, Journal ofCloud Computing, 12(1), 2023, 24.
  26. [26] B. Song, Z. Wang, and L. Zou, An improved PSO algorithmfor smooth path planning of mobile robots using continuoushigh-degree Bezier curve, Applied Soft Computing, 100, 2021,106960.
  27. [27] W. Xu, R. Zhang, and L. Chen, An improved crow searchalgorithm based on oppositional forgetting learning, AppliedIntelligence, 52(7), 2022, 7905–7921.
  28. [28] K. Hussain, M.N.M. Salleh, S. Cheng, and Y. Shi, Onthe exploration and exploitation in popular swarm-basedmetaheuristic algorithms, Neural Computing and Applications,31(11), 2019, 7665–7683.
  29. [29] B. Morales-Casta˜neda, D. Zald´ıvar, E. Cuevas, F. Fausto, andA. Rodr´ıguez, A better balance in metaheuristic algorithms:Does it exist? Swarm and Evolutionary Computation, 54, 2020,100671.

Important Links:

Go Back