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A hybrid multi-objective firefly algorithm for big data optimization
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DOI：10.1016/j.asoc.2017.06.029.png)
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Multi-objective evolutionary algorithms (MOEAs) have shown good performance on many benchmark and real world multi-objective optimization problems. However, MOEAs may suffer from some difficulties when solving big data optimization problems with thousands of variables. Firefly algorithm (FA) is a new meta-heuristic, which has been proved to be a good optimization tool. In this paper, we present a hybrid multi-objective FA (HMOFA) for big data optimization. A set of big data optimization problems, including six single objective problems and six multi-objective problems, are tested in the experiments. Computational results show that HMOFA achieves promising performance on all test problems. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Firefly algorithm (FA)
Multi-objective firefly algorithm
Multi-objective optimization
Big data optimization
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6.6
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1.4W
被引数:
4.8W
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