Abstract
This paper combines the dynamically adjusting parameters, the chaos search (CS), and the modified simplex method (MS) with GAFSA, and the CS_MS_GAFSA is proposed. The algorithm speeds up the convergence by dynamically adjusting the parameters, and increases the probability of artificial fish esca** local extreme points by chaotic search for the current global optimum value. When the algorithm converges to the global optimum nearby, a simplex is constructed and the algorithm switches to MS which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on benchmark functions show that CS_MS_GAFSA does improve in optimizing accuracy and convergence speed.
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References
Li XL, Qian JX (2003) Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques. J Circuits Syst 2(1):1–6 (in Chinese)
Jiang MY, Yuan DF (2012) Artificial fish swarm algorithm and its application. Science Press, Bei**g (in Chinese)
Jiang MY, Cheng YM, Yuan DF (2014) Improved artificial fish swarm algorithm, 5th edn
Ma XM, Liu N (2014) Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem. J Commun 1, 35(1):1–6
Zhu KC, Jiang MY (2009) An improved artificial fish swarm algorithm based on chaotic search and feedback strategy. In: Proceedings of international conference on computational intelligence and software engineering (CISE), 2009, 1–4
Zhu KC, Jiang MY (2010) Quantum artificial fish swarm algorithm. In: Proceedings of the 8th world congress on intelligent control and automation, 2010, pp 1–5
Jiang MY, Cheng YM (2010) Simulated annealing artificial fish swarm algorithm. In: 8th world congress on intelligent control and automation, WCICA, 2010, pp 1590–1593
Zhang P, Wei P, Yu HY, Fei C (2012) Biogeography-based optimization algorithm by using chaotic search. J Univ Electron Sci Technol China 41(1):65–69
Liu H, Huang M, Huang D (2005) Survey on optimization method based on chaotic search and its developments. J Nan**g Univ Sci Technol 124–128
Bucolo M, Caponetto L Fortuna et al (2002) Does chaos work better than niose? IEEE Circuits Syst Mag 2(3):4–19
Yang XQ, Teo KL, Caccetta L (2001) Optimization methods and applications. Kluwer Academic Publishers, Boston
Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Acknowledgments
This work was part of the program on The National Natural Science Foundation of China funded under the National Natural Science Fund Committee, grant number 61374198.
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Peng, Pz., Yuan, J., Wang, Zj., Yu, Y., Jiang, M. (2015). An Improved GAFSA Based on Chaos Search and Modified Simplex Method. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_14
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DOI: https://doi.org/10.1007/978-3-662-46469-4_14
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