关键词: 果蝇算法, 味道浓度判定值, 粒子群算法, 差分进化, 煤气化

Abstract:

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.

Key words: FOA, smell concentration judgment, PSO, differential evolution, coal gasification