Abstract
Moth-flame optimization algorithm has the demerit of being easily trapped in local optimum. To solve this problem, an improved algorithm ASMFO is proposed in this paper. Adaptive weight can be automatically changed so that the algorithm can get a greater search scope in the early stage and the precision of the optimal solution can be increased in the later stage of the algorithm. Moreover, the simulated annealing method is employed to accept new solutions with a certain probability, which can further alleviate the problem that MFO is easy to fall into local optimum and will also enhance the global search ability of MFO algorithm. The experimental results show that the improved algorithm is superior to other optimization algorithms in the convergence precision and the stability.
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Zhang, Q., Liu, L., Li, C., Jiang, F. (2018). Moth-Flame Optimization Algorithm Based on Adaptive Weight and Simulated Annealing. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_14
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DOI: https://doi.org/10.1007/978-3-030-02698-1_14
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