Differential Evolution Algorithm Based on Sharing Learning

In order to alleviate premature convergence in traditional differential evolution algorithms, a differential evolution algorithm based on sharing learning strategy (SLDE) was proposed and the concepts of sharing-individual (SI) and sharing learning factor were introduced in SLDE. The sharing-individual covers the whole population, while the superior individuals guide the promising searching direction, the inferior individuals maintain the population diversity in the evolution process. By learning from the sharing-individual, information exchange was achieved among the whole population to avoid missing information of individuals, which helps the algorithm jump over the trap of local optimal solution and improve the local and global exploration capability. Meanwhile, the evolutionary information of individuals was made full of use in SLDE, and the sharing learning factor was self-adaptively adjusted according to the distance of fitness value of individual and the optimal fitness value, in order to alleviate the randomness and blindness from the random individuals and enhance the searching ability. A total of 22 Benchmark test functions with different properties were used for performance test comparison with seven state-of-the-art DE variants. The experimental results showed that SLDE has strong ability to escape from local optima, significantly reduce the evolutionary generations and greatly improve the convergence precision, convergence speed and stability. The overall global optimization performance of SLDE is much better than other improved DE algorithms. © 2019, Editorial Department of Advanced Engineering Sciences. All right reserved.