Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization

The expression of the smell concentration judgment value is significantly important in the application of the fruit fly optimization algorithm (FOA). The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solutions that are not zero. This paper proposes an improved fruit fly optimization algorithm based on differential evolution (DFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. Through numerical experiments based on 12 benchmark instances, experimental results show that the improved DFOA has a stronger global search ability, faster convergence, and convergence stability in high-dimensional functions than the original FOA and evolutionary algorithms from literature. The DFOA is also applied to optimize the operation of the Texaco gasification process by maximizing the syngas yield using two decision variables, i.e., oxygen–coal ratio and coal concentration. The results show that DFOA can quickly get the optimal output, demonstrating the effectiveness of DFOA.