Abstract
In this paper, we study the fruit fly in the fruit fly optimization algorithm (FOA) system moving in a quantum multi-dimensional space and propose a quantum behaved fruit fly optimization algorithm (QFOA) for the continuous function optimization problem. Computational experiments and comparisons are carried out based on a set of benchmark functions. The computational results show the advantage of QFOA to the original FOA.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61703012) and Beijing Natural Science Foundation (No. 4182010).
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Zhang, X., Xia, S. (2019). Quantum Behaved Fruit Fly Optimization Algorithm for Continuous Function Optimization Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_31
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DOI: https://doi.org/10.1007/978-3-030-26369-0_31
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