Abstract
We propose an acceleration method for the fireworks algorithms which uses a convergence point for the population estimated from moving vectors between parent individuals and their sparks. To improve the accuracy of the estimated convergence point, we propose a new type of firework, the synthetic firework, to obtain the correct of the local/global optimum in its local area’s fitness landscape. The synthetic firework is calculated by the weighting moving vectors between a firework and each of its sparks. Then, they are used to estimate a convergence point which may replace the worst firework individual in the next generation. We design a controlled experiment for evaluating the proposed strategy and apply it to 20 CEC2013 benchmark functions of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs each. The experimental results and the Wilcoxon signed-rank test confirm that the proposed method can significantly improve the performance of the canonical firework algorithm.
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References
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44
Zheng, S.Q., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 2069–2077, June 2013
Zheng, S.Q., Janecek, A., Li, J.Z., Tan, Y.: Dynamic search in fireworks algorithm. In: IEEE Congress on Evolutionary Computation, Beijing, China, pp. 3222–3229, July 2014
Yu, J., Takagi, H.: Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. In: International Conference on Swarm Intelligence, Fukuoka, Japan, pp. 477–484, July 2017
Luigi, M., Matteo, M.: Robust estimation of natural gradient in optimization by regularized linear regression. In: 1st International SEE Conference on Geometric Science of Information, Paris, France, pp. 861–867, August 2013
Schtze, O., Alvarado, S., Segura, C., Landa, R.: Gradient subspace approximation: a direct search method for memetic computing. Soft Comput. 21(21), 6331–6350 (2017)
Murata, N., Nishii, R., Takagi, H., Pei, Y.: Estimation methods of the convergence point of moving vectors between generations. In: Japanese Society for Evolutionary Computation Symposium 2014, Hatsukaichi, Japan, pp. 210–215, December 2014. (in Japanese)
Murata, N., Nishii, R., Takagi, H., Pei, Y.: Analytical estimation of the convergence point of populations. In: 2015 IEEE Congress on Evolutionary Computation, Sendai, Japan, pp. 2619–2624, May 2015
Liang, J.J., Qu, B.Y., Suganthan, P.N., Alfredo, G.H.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization (2013). http://al-roomi.org/multimedia/CEC_Database/CEC2013/RealParameterOptimization/CEC2013_RealParameterOptimization_TechnicalReport.pdf
Yu, J., Takagi, H.: Estimation of the convergence points of a population using an individual pool. In: 10th International Workshop on Computational Intelligence and Applications, Hiroshima, Japan, pp. 67–72, November 2017
Acknowledgment
This work was supported in part by Grant-in-Aid for Scientific Research (JP15K00340) and the Natural Science Foundation of China (NSFC) under grant no. 61673025.
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Yu, J., Takagi, H., Tan, Y. (2018). Accelerating the Fireworks Algorithm with an Estimated Convergence Point. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_26
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DOI: https://doi.org/10.1007/978-3-319-93815-8_26
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