Abstract：Introducing the velocity inertia, memory capacity of each individual and learning or communicating capacity of Particle Swarm Optimization (PSO) into the Artificial Fish-Swarm Algorithm (AFSA), a new algorithm called the “Fish-Swarm Algorithm optimized by PSO(PSO-FSA)” was put forward. In this new algorithm, the swimming of each fish has velocity inertia, and the PSO-FSA has totally five kinds of behavior pattern as follows: swarming, following, remembering, communicating and searching. The simulation analysis shows that PSO-FSA has more stable and higher performance in convergence speed and searching precision than PSO and AFSA. Finally, the PSO-FSA was applied to the maximum power point tracking of photovoltaic power generation system under partially shaded condition, and the experimental results show that PSO-FSA can find the maximum power point under partially shaded insolation conditions quickly and precisely.
段其昌 唐若笠 隆霞. 粒子群优化鱼群算法及其在光伏系统最大功率点跟踪中的应用[J]. 计算机应用, 2012, 32(12): 3299-3302.
DUAN Qi-chang TANG Ruo-li LONG Xia. Fish swarm algorithm optimized by PSO applied in maximum power point tracking of photovoltaic power system. Journal of Computer Applications, 2012, 32(12): 3299-3302.
2012, Vol. 32 
