Gaussian Barebones Differential Evolution with Random-type Gaussian Mutation Strategy

This study attempts to propose a random-type Gaussian mutation strategy to improve the solution accuracy of Gaussian barebones differential evolution (GBDE). The proposed Gaussian mutation strategy is not only parameter free, but also employed to enhance the population diversity and global searching ability of the original mutation strategy. The search performance of GBDE with the proposed mutation strategy is compared with two standard DEs (DE/rand/1 and DE/best/1), the original GBDE and its modified version in terms of solution accuracy. Simulation results on two real-world optimal control problems given in IEEE - CEC 2011 evolutionary algorithm competition demonstrate the effectiveness of the proposed GBDE algorithm.