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Algorithms 2014, 7(1), 15-31; doi:10.3390/a7010015
Article

Bio-Inspired Meta-Heuristics for Emergency Transportation Problems

1,2, 1 and 1,*
Received: 4 December 2013; in revised form: 28 January 2014 / Accepted: 30 January 2014 / Published: 11 February 2014
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Abstract: Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO) algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem.
Keywords: bio-inspired algorithms; transportation problems; planning and scheduling; biogeography-based optimization (BBO) bio-inspired algorithms; transportation problems; planning and scheduling; biogeography-based optimization (BBO)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zhang, M.-X.; Zhang, B.; Zheng, Y.-J. Bio-Inspired Meta-Heuristics for Emergency Transportation Problems. Algorithms 2014, 7, 15-31.

AMA Style

Zhang M-X, Zhang B, Zheng Y-J. Bio-Inspired Meta-Heuristics for Emergency Transportation Problems. Algorithms. 2014; 7(1):15-31.

Chicago/Turabian Style

Zhang, Min-Xia; Zhang, Bei; Zheng, Yu-Jun. 2014. "Bio-Inspired Meta-Heuristics for Emergency Transportation Problems." Algorithms 7, no. 1: 15-31.

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