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. 2014 Mar 20:2014:232704.
doi: 10.1155/2014/232704. eCollection 2014.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning

Bai Li  1 , Li-gang Gong  2 , Wen-lun Yang  1
Affiliations

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning

Bai Li et al. ScientificWorldJournal. .

Abstract

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

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Figures

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Figure 1
Figure 1
Schematic diagram of combat field modeling.
Figure 2
Figure 2
Schematic diagram of flight cost computation.
Figure 3
Figure 3
Comparative path planning results optimized by different ABC relevant algorithms in Case 1 (D = 20).
Figure 4
Figure 4
Comparative path planning results optimized by different ABC relevant algorithms in Case 1 (D = 50).
Figure 5
Figure 5
Comparative convergence curves of ABC, I-ABC, IF-ABC, and BE-ABC in Case 1 (D = 20 and MCN = 500).
Figure 6
Figure 6
Comparative convergence curves of ABC, I-ABC, IF-ABC, and BE-ABC in Case 1 (D = 50 and MCN = 500).
Figure 7
Figure 7
Comparative path planning results optimized by different ABC relevant algorithms in Case 2 (D = 30 and MCN = 1000).
Figure 8
Figure 8
Comparative convergence curves of ABC, I-ABC, IF-ABC, and BE-ABC in Case 2 (D = 30 and MCN = 1000).
Figure 9
Figure 9
Comparative path planning results optimized by different ABC relevant algorithms in Case 3 (D = 30 and MCN = 1000).
Figure 10
Figure 10
Comparative convergence curves of ABC, I-ABC, IF-ABC, and BE-ABC in Case 3 (D = 30 and MCN = 1000).
See this image and copyright information in PMC

References

    1. Zhang Y, Jun Y, Wei G, Wu L. Find multi-objective paths in stochastic networks via chaotic immune PSO. Expert Systems with Applications. 2010;37(3):1911–1919.
    1. Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics. 2013;9:132–141.
    1. Zhang Y, Agarwal P, Bhatnagar V, Balochian S, Yan J. Swarm intelligence and its applications. The Scientific World Journal. 2013;2013:3 pages.528069 - PMC - PubMed
    1. Li B, Gong LG, Zhao CH. Unmanned combat aerial vehicles path planning using a novel probability density model based on Artificial Bee Colony algorithm. Proceedings of the International Conference on Intelligent Control and Information Processing (ICICIP '13); June 2013; Beijing, China. pp. 620–625.
    1. MacHaret DG, Neto AA, Campos MFM. Proceedings of the Advances in Artificial Intelligence Conference (SBIA '10) Vol. 6404. Berlin, Germany: Springer; 2010. Feasible UAV path planning using genetic algorithms and Bézier curves; pp. 223–232.

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