Abstract
Firefly algorithm is a nature-inspired, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence and trap in local optimum easily on many optimization problems. Therefore, we propose a new FA variant, called LRRSFA, which aims to solve the problem of premature convergence and local optimum. LRFA mainly has three changed points. First, the fixed original attractiveness is replaced by random variable attractiveness. Second, it is neighborhood search of global optimal particle. Third, some particle move towards given particles which are chosen while initialing population in specific condition. Results tested on eleven standard benchmark function are better than standard firefly algorithm.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China under Grant (Nos. 51669014, 61663029), Science Foundation of Jiangxi Province under Grant (No. 20161BAB212037), Jiangxi Province Department of Education Science and Technology Project under Grant (No. GJJ151133).
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Xie, Z., Zhao, J., Sun, H., Wang, H., Wang, K. (2017). Local-Learning and Reverse-Learning Firefly Algorithm. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_18
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DOI: https://doi.org/10.1007/978-3-319-48490-7_18
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