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Engineering Applications of Artificial Intelligence


This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling.


This material is based upon work supported by the National Science Foundation under Grant no. 1344954, the National Natural Science Foundation of China under Grant nos. 61305078, 61074032 and 61179041, and the Shaoxing City Public Technology Applied Research Project under Grant no. 2013B70004.

Original Citation

H. Ma, S. Su, D. Simon and M. Fei, "Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling," Eng Appl Artif Intell, vol. 44, no. 9, pp. 79-90, 2015.