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A New Hybrid Firefly Algorithm for Complex and Nonlinear Problem

机译：复杂和非线性问题的一种新的混合萤火虫算法

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摘要

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.
机译：全局优化方法在解决许多现实问题中发挥着重要作用。但是，对于高维和非线性问题，单一方法的实施过度预防，特别是在寻找最佳解决方案的准确性和收敛速度性能方面。近年来，混合优化方法已显示出克服此类挑战的潜在成就。本文提出了一种新的混合优化方法，称为混合进化萤火虫算法（HEFA）。该方法将标准萤火虫算法（FA）与差分进化（DE）方法的进化运算相结合，以提高萤火虫之间的搜索精度和信息共享。 HEFA方法用于估计复杂的非线性生物学模型中的参数，以解决其在高维和非线性问题中的有效性。实验结果表明，与现有方法相比，该方法找到最佳解的准确性和收敛速度性能要好得多。

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