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Bio-Inspired Meta-Heuristics for Emergency Transportation Problems

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College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China
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College of Computer Science & Technology, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China
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Author to whom correspondence should be addressed.
Algorithms 2014, 7(1), 15-31; https://doi.org/10.3390/a7010015
Received: 4 December 2013 / Revised: 28 January 2014 / Accepted: 30 January 2014 / Published: 11 February 2014
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. View Full-Text
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)
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.

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