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