A New Hybrid Firefly Algorithm for Complex and Nonlinear Problem
Global optimization methods play an important role to solve many real-world problems. However, the implementation of single methods is excessively preventive for high dimensionality and nonlinear problems, especially in term of the accuracy of finding best solutions and convergence speed performance. In recent years, hybrid optimization methods have shown potential achievements to overcome such challenges. In this paper, a new hybrid optimization method called Hybrid Evolutionary Firefly Algorithm (HEFA) is proposed. The method combines the standard Firefly Algorithm (FA) with the evolutionary operations of Differential Evolution (DE) method to improve the searching accuracy and information sharing among the fireflies. The HEFA method is used to estimate the parameters in a complex and nonlinear biological model to address its effectiveness in high dimensional and nonlinear problem. Experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed method is significantly better compared to those achieved by the existing methods.
KeywordsFirefly Algorithm Differential Evolution hybrid optimization parameter estimation biological model
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- 2.Shao, Z., Gao, S., Wang, S.: A Hybrid Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Stochastic Travel Time. In: Fuzzy Info. and Engineering, ASC, vol. 54, pp. 566–574 (2009)Google Scholar
- 3.dos Santos Coelho, L., Mariani, V.: Combining of Differential Evolution and Implicit Filtering Algorithm Applied to Electromagnetic Design Optimization. In: Soft Computing in Industrial Applications, ACS, vol. 39, pp. 233–240 (2007)Google Scholar
- 5.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 6.Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution. Wiley (1966)Google Scholar
- 7.Lillacci, G., Khammash, M.: Parameter estimation and model selection in computational biology. PLoS Computational Biology 6(3), e1000696 (2010)Google Scholar
- 8.Das, S., Abraham, A., Konar, A.: Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspective. SCI, vol. 116, pp. 1–38 (2008)Google Scholar