Abstract
The search behavior of firefly algorithm (FA) is determined by the attractions among fireflies. In the standard FA and its most modifications, worse fireflies can move toward other better ones, while better fireflies seldom move to other positions. To enhance the search of better fireflies, this paper presents a hybrid firefly algorithm (HFA), Which employs a local search operator inspired by differential evolution (DE). Moreover, the control parameters are dynamically adjusted during the search process. Experiments are conducted on thirteen continuous optimization problems. Computational results show that HFA achieves better solutions than the standard FA and three other improved FA variants.
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Acknowledgement
This work is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), the Humanity and Social Science Foundation of Ministry of Education of China (No. 13YJCZH174), the National Natural Science Foundation of China (Nos. 61305150, 61261039, and 61461032), the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ151099), the Natural Science Foundation of Jiangxi Province (No. 20142BAB217020), and the Student Research Training Program of Nanchang Institute of Technology (No.53).
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Wang, W. et al. (2016). A Hybrid Firefly Algorithm for Continuous Optimization Problems. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_46
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DOI: https://doi.org/10.1007/978-3-319-48674-1_46
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