Improved differential evolution algorithms for handling noisy optimization problems

Differential evolution (DE) is a simple and efficient algorithm for function optimization over continuous spaces. It has reportedly outperformed many types of evolutionary algorithms and other search heuristics when tested over both benchmark and real-world problems. However, the performance of DE deteriorates severely if the fitness function is noisy and continuously changing. In this paper two improved DE algorithms have been proposed that can efficiently find the global optima of noisy functions. This is achieved firstly by weighing the difference vector by a random scale factor and secondly by employing two novel selection strategies as opposed to the conventional one used in the original versions of DE. An extensive performance comparison of the newly proposed scheme, the original DE (DE/Rand/1/Exp), the canonical PSO and the standard real-coded EA has been presented using well-known benchmarks corrupted by zero-mean Gaussian noise. It has been found that the proposed method outperforms the others in a statistically significant way.