Authors: Xiangtao Li Minghao Yin
Publish Date: 2014/07/22
Volume: 31, Issue: 2, Pages: 546-576
Abstract
The differential evolution algorithm DE is a simple and effective global optimization algorithm It has been successfully applied to solve a wide range of realworld optimization problem In this paper the proposed algorithm uses two mutation rules based on the rand and best individuals among the entire population In order to balance the exploitation and exploration of the algorithm two new rules are combined through a probability rule Then selfadaptive parameter setting is introduced as uniformly random numbers to enhance the diversity of the population based on the relative success number of the proposed two new parameters in a previous period In other aspects our algorithm has a very simple structure and thus it is easy to implement To verify the performance of MDE 16 benchmark functions chosen from literature are employed The results show that the proposed MDE algorithm clearly outperforms the standard differential evolution algorithm with six different parameter settings Compared with some evolution algorithms ODE OXDE SaDE JADE jDE CoDE CLPSO CMAES GL25 AFEP MSAEP and ENAEP from literature experimental results indicate that the proposed algorithm performs better than or at least comparable to stateoftheart approaches from literature when considering the quality of the solution obtained
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