Abstract
The Gravitational Search Algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive Gravitational Search Algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA.
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