For the demerits of fruit fly optimization algorithm(FOA)

such as easily falling into local optimum

slow convergence rate and low convergence precision

an improved FOA is proposed called multi-strategy fruit fly optimization algorithm. It is based on social cognitive part of particle swarm optimization algorithm (PSO) and the differential vector of differential evolution (DE). For those individuals whose smell concentration values are worse than the average concentration

using social cognition operator generates next generation to accelerate the convergence rate. For others

introducing the differential vector improves the ability to jump out the local optimum. Through the simulation on eight benchmarks and comparison with other algorithms

the experimental results show that SFOA has better global search capability

fast convergence and higher convergence precision. Finally the SFOA is also applied to optimize the operation of a GE gasification process

which is to maximize the syngas yield with two decision variables

i.e.

oxygen-coal ratio and coal concentration. The results show that SFOA can quickly find the optimal value

which demonstrates the effectiveness of SFOA.