Abstract
Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, like other intelligent algorithms, the firefly algorithm still has some shortcomings, such as the algorithm is easy to fall into the local optimal, and the convergence speed is slow in the later period. Therefore, this paper proposes a new firefly algorithm with dynamic step change strategy (DsFA) to balance the global and local search capabilities. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, the computational results show that DsFA is more efficient than many other FA algorithms.
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Acknowledgments
The authors would like to thank anonymous reviewers for their detailed and constructive comments that help us to increase the quality of this work. This work was supported by the National Natural Science Foundation of China (No.: 61866014, 61862027 and 61962024), the National Natural Science Foundation of Jiangxi (No.: 20192BAB207032).
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Wang, J., Song, F., Yin, A., Chen, H. (2020). Firefly Algorithm Based on Dynamic Step Change Strategy. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_31
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DOI: https://doi.org/10.1007/978-3-030-62460-6_31
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