Performance enhancement of the differential evolution algorithm using local search and a self-adaptive scaling factor

This paper presents a novel differential evolution (DE) algorithm using a dynamic strategy, local search, and a self-adaptive scaling factor (DELSBP ) to enhance the performance of the traditional DE algorithm. The DELSBP consists of a dynamic strategy, which updates the optimal vector instantaneously, and back-propagation-based local search, which enhances its searching capability from the corresponding neighborhood. In addition, a self-adaptive scaling factor strategy, developed using a fuzzy logic system, is introduced to accelerate the convergence velocity. The inputs of fuzzy systems incorporate the change in fitness values and generation number to calculate a change in the scaling factor. The performance of the DELSBP algorithm has been demonstrated using experimental results from a set of standard test functions and through the estimation of coefficients for an IIR filter with noise. These results demonstrate the performance and efficacy of the DELSBP algorithm.

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