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An effective and efficient fruit fly optimization algorithm with level probability policy and its applications
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DOI：10.1016/j.knosys.2016.01.006.png)
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An improved fruit fly optimization algorithm (FOA) is proposed for optimizing continuous function problems and handling joint replenishment problems (JRPs). In the proposed FOA, a level probability policy and a new mutation parameter are developed to balance the population diversity and stability. Twenty-nine complex continuous benchmark functions are used to verify the performance of the FOA with level probability policy (LP-FOA). Numerical results show that the proposed LP-FOA outperforms two state-of-the-art variants of FOA, the differential evolution algorithm and particle swarm optimization algorithm, in terms of the median and standard deviations. The LP-FOA with a new and delicate coding style is also used to handle the classic JRP, which is a complex combinatorial optimization problem. Experimental results reveal that LP-FOA is better than the current best intelligent algorithm, particularly for large-scale JRPs. Thus, the proposed LP-FOA is a potential tool for various complex optimization problems. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Fruit fly optimization algorithm
Level probability
Continuous function problem
Joint replenishment problems
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