Abstract
Inspired by Gaussian barebones differential evolution (GBDE), this study attempts to propose a new Gaussian mutation strategy, termed by GBDE/best-rand, to improve the solution accuracy. This study also proposes a hybrid crossover strategy, the hybridization of the binomial and arithmetic crossover strategies, for differential evolution (DE) to further balance the global search ability and convergence rate. The GBDE with the proposed Gaussian mutation and hybrid crossover strategies is also almost parameter free. The search performance of the proposed modified GBDEs is compared with two standard DEs (DE/rand/1 and DE/best/1), the GBDE and its modified version in terms of solution accuracy, convergence speed and reliability. Simulation results demonstrate the effectiveness of the proposed modified GBDE algorithm.
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