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UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

Author

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  • Yudong Zhang
  • Lenan Wu
  • Shuihua Wang

Abstract

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

Suggested Citation

  • Yudong Zhang & Lenan Wu & Shuihua Wang, 2013. "UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:705238
    DOI: 10.1155/2013/705238
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    Cited by:

    1. J. Uthayakumar & Noura Metawa & K. Shankar & S. K. Lakshmanaprabu, 2020. "RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques," Information Systems and e-Business Management, Springer, vol. 18(4), pages 617-645, December.
    2. Qi You & Jun Sun & Feng Pan & Vasile Palade & Bilal Ahmad, 2021. "DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control," Mathematics, MDPI, vol. 9(16), pages 1-20, August.

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