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Differential Evolution With Auto-Enhanced Population Diversity

In differential evolution (DE) studies, there are many parameter adaptation methods, aiming at tuning the mutation factor F and the crossover probability CR. However, these methods still cannot resolve the issues of population premature convergence and population stagnation. To address these issues, in this paper, we investigate the population adaptation regarding population diversity at the dimensional level and propose a mechanism named auto-enhanced population diversity (AEPD) to automatically enhance population diversity. AEPD is able to identify the moments when a population becomes converging or stagnating by measuring the distribution of the population in each dimension. When convergence or stagnation is identified at a dimension, the population is diversified at that dimension to an appropriate level or to eliminate the stagnation issue. The AEPD mechanism was incorporated into a popular DE algorithm and it was tested on a set of 25 CEC2005 benchmark functions. The results showed that AEPD significantly improved the performance of the original algorithms. In addition, AEPD helped the algorithms become less sensitive to population size, a parameter widely considered problem dependent for many DE algorithms. The DE algorithm with AEPD also has a superior performance in comparison with several other peer algorithms.