Abstract
A self-adaptive fruit fly optimization (SFFO) algorithm is presented for solving high-dimensional global optimization problems. Unlike the conventional self-adaptive swarm intelligence algorithms that try to modify the values of control parameters during the run by taking the actual search process into account, the proposed SFFO algorithm self-adaptively adjusts its search along an appropriate decision variable from its previous experience in generating promising solutions. The presented self-adaptive method significantly improves the intensive search capability of the fruit fly optimization algorithm around promising areas that are problem and search process dependent. Extensive computational simulations and comparisons are performed based on a set of 40 benchmark functions from the literature. The computational results show that the proposed SFFO is a new state-of-the-art algorithm for global optimization.
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Acknowledgements
This research is partially supported by the National Science Foundation of China (51575212, 61503170, 61603169), A Project of Shandong Province Higher Educational Science and Technology Program (J14LN28), Shanghai Key Laboratory of Power station Automation Technology.
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Sang, HY., Pan, QK. & Duan, Py. Self-adaptive fruit fly optimizer for global optimization. Nat Comput 18, 785–813 (2019). https://doi.org/10.1007/s11047-016-9604-z
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DOI: https://doi.org/10.1007/s11047-016-9604-z