Swarm Automated Planning Algorithm with Repairing Operators

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Abstract:

Swarm automated planning algorithm is a planning method based on planning graph technology, and the algorithm which could improve the searching efficiency through importing swarm intelligence has the character of global and parallel. Yet sometimes the algorithm has the shortcoming in the local searching. For instance, maybe the quality of the best candidate planning solution would get lower after another around of searching. To enhance the ability of the local searching and the convergence acceleration, the swarm automated planning algorithm which with the local repairing operators is presented in this paper. The searching efficiency could be bettered through the pertinence repairing operators and the heuristic evaluation information to control the repairing processes.

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1426-1429

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July 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] ZENG Bi, YANG Yimin. Method of real_time path planning based on ant colony algorithm in dynamic environment. Application Research of Computers. 2010, 27(3): 860-863.

Google Scholar

[2] JIN Feihu, GUO Qi. Research of multi-space station path planning using Hopfield neural network based on ant colony system. Application Research of Computers. 2010, 27(1): 860-863.

Google Scholar

[3] Serap Ulusam Sec_kiner, Mustafa Kurt. Ant colony optimization for the job rotation scheduling problem[J]. Applied Mathematics and Computation. 2008, 201(1-2): 149-160.

DOI: 10.1016/j.amc.2007.12.006

Google Scholar

[4] Kwee Kim Lim, Yew-Soon Ong, Meng Hiot Lim, etc. Hybrid ant colony algorithms for path planning in sparse graphs[J]. Soft Computing. 2008, 12(10): 981–994.

DOI: 10.1007/s00500-007-0264-x

Google Scholar

[5] CHAI Xiaolong etc. Ant colony planning algorithm based on planning graph. Journal of Computer Research and Development. 2009, 46(9): 1471-1479.

Google Scholar