自适应差分演化算法及其在全局优化中的应用

Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

差分演化算法(DE)是一类基于种群的启发式全局搜索技术,对于实值参数的优化具有很强的鲁棒性。为了提高差分演化算法的寻优速度、克服启发式算法常见的早熟收敛问题,对差分演化算法进行了改进,提出一种的新的自适应差分演化算法(称为ISADE)。其结果表明,新算法在解的精度、稳定性和收敛性上表现出很好的性能。

Differential evolution（DE） is a new heuristic global searchig technique based on population which has been found to be very robust for real parameter＇s optimization.In order to accelerate the convergence speed of DE algorithm in optimal searching and to overcome the premature convergence which is frequently existed in heuristic algorithms,in this paper,we propose an improved selfadaptive differential evolution,referred to as ISADE.Experimental results indicate that the performance of the ISADE outperforms other evolutionary algorithms in terms of the quality of the final solution and the stability;and its computational cost is lower than the cost required by the other techniques compared.