Hybrid differential evolution for noisy optimization

A robust hybrid algorithm named DEOSA for function optimization problems is investigated in this paper. In recent years, differential evolution (DE) has attracted wide research and effective applications in various fields. However, to the best of our knowledge, most of the available works did not consider noisy and uncertain environments in practical optimization problems. This paper focuses on a robust DE, which can adapt to noisy environment in real applications. By combining the advantages of DE algorithm, the optimal computing budget allocation (OCBA) technique and simulated annealing (SA) algorithm, a robust hybrid DE approach DEOSA is proposed. In DEOSA, the population-based search mechanism of DE is applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, SA is also applied in the hybrid approach to maintain the diversity of the population, in order to alleviate the negative influences on greedy selection mechanism of DE brought by the noises. DEOSA is tested by well-known benchmark problems with noise and the effect of noise magnitude is also investigated. The comparisons to several commonly used techniques for optimization in noisy environment are also carried out. The results and comparisons demonstrate the superiority of DEOSA.