Abstract
The performance of firefly algorithm (FA) is seriously affected by its parameters. Recently, we proposed a new FA with adaptive control parameters (ApFA), in which the step factor is dynamically updated and the attractiveness oscillates in a fixed interval. In this paper, we present a modified version of ApFA, namely MApFA, which introduces a new strategy to change the attractiveness. Simulation results on several benchmark functions show that MApFA can achieve more accurate solution than ApFA.
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References
Sun, H., Wang, K., Zhao, J., Yu, X.: Artificial bee colony algorithm with improved special centre. Int. J. Comput. Sci. Math. 7(6), 548–553 (2016)
Yun, G.: A new multi-population-based artificial bee colony for numerical optimization. Int. J. Comput. Sci. Math. 7(6), 509–515 (2016)
Lv, L., Wu, L.Y., Zhao, J., Wang, H., Wu, R.X., Fan, T.H., Hu, M., Xie, Z.F.: Improved multi-strategy artificial bee colony algorithm. Int. J. Comput. Sci. Math. 7(5), 467–475 (2016)
Lu, Y., Li, R.X., Li, S.M.: Artificial bee colony with bidirectional search. Int. J. Comput. Sci. Math. 7(6), 586–593 (2016)
Cai, X., Gao, X.Z., Xue, Y.: Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspired Comput. 8(4), 205–214 (2016)
Xue, F., Cai, Y., Cao, Y., Cui, Z., Li, F.: Optimal parameter settings for bat algorithm. Int. J. Bio-Inspired Comput. 7(2), 125–128 (2015)
Cai, X., Wang, L., Kang, Q., Wu, Q.: Bat algorithm with Gaussian walk. Int. J. Bio-Inspired Comput. 6(3), 166–174 (2014)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)
Wang, H., Wang, W.J., Sun, H., Rahnamayan, S.: Firefly algorithm with random attraction. Int. J. Bio-Inspired Comput. 8(1), 33–41 (2016)
Wang, H., Wang, W.J., Zhou, X.Y., Sun, H., Zhao, J., Yu, X., Cui, Z.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382–383, 374–387 (2017)
Cui, Z., Sun, B., Wang, G., Xue, Y.: A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J. Parallel Distrib. Comput. 103, 42–52 (2017)
Wang, G.G., Gandomi, A.H., Yang, X.S., Alavi, A.H.: A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int. J. Bio-Inspired Comput. 8(5), 286–299 (2016)
Zhang, Y.W., Wu, J.T., Guo, X., Li, G.N.: Optimising web service composition based on differential fruit fly optimisation algorithm. Int. J. Comput. Sci. Math. 7(1), 87–101 (2016)
Cui, Z., Fan, S., Zeng, J., Shi, Z.Z.: APOA with parabola model for directing orbits of chaotic systems. Int. J. Bio-Inspired Comput. 5(1), 67–72 (2013)
Cui, Z., Fan, S., Zeng, J., Shi, Z.Z.: Artificial plant optimisation algorithm with three-period photosynthesis. Int. J. Bio-Inspired Comput. 5(2), 133–139 (2013)
Fister Jr., I., Yang, X.S., Fister, I., Brest, J., Memetic firefly algorithm for combinatorial optimization (2012). arXiv preprint arXiv:1204.5165
Wang, H., Cui, Z.H., Sun, H., Rahnamayan, S., Yang, X.S.: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Comput. 21(18), 5325–5339 (2017). doi:10.1007/s00500-016-2116-z
Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)
Wang, H., Zhou, X.Y., Sun, H., Yu, X., Zhao, J., Zhang, H., Cui, L.Z.: Firefly algorithm with adaptive control parameters. Soft Comput. 21(17), 5091–5102 (2017). doi:10.1007/s00500-016-2104-3
Wang, H., Wu, Z.J., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181(20), 4699–4714 (2011)
Wang, H., Rahnamayan, S., Sun, H., Omran, M.G.H.: Gaussian bare-bones differential evolution. IEEE Trans. Cybern. 43(2), 634–647 (2013)
Guo, Z.L., Wang, S.W., Yue, X.Z., Yin, B.: Enhanced social emotional optimisation algorithm with elite multi-parent crossover. Int. J. Comput. Sci. Math. 7(6), 568–574 (2016)
Yu, G.: An improved firefly algorithm based on probabilistic attraction. Int. J. Comput. Sci. Math. 7(6), 530–536 (2016)
Xue, Y., Jiang, J.M., Zhao, B.P., Ma, T.H.: A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput. (2017, in press). doi:10.1007/s00500-017-2547-1
Acknowledgements
This work is supported by the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ161115), the National Natural Science Foundation of China (No. 61663028), the Natural Science Foundation of Jiangxi Province (No. 20171BAB202035), and the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing (No. 2016WICSIP015).
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Wang, W., Wang, H., Zhao, J., Lv, L. (2017). Adaptive Firefly Algorithm with a Modified Attractiveness Strategy. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_62
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DOI: https://doi.org/10.1007/978-3-319-68542-7_62
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