Fruit Fly Optimization Algorithm Based on History Cognition
Han Jun-yin · Jisuanji kexue yu tansuo · 2014
Considering the problem of premature convergence in fruit fly optimization algorithm(FOA),this paper proposes a new FOA based on cognition(FOABHC).The new algorithm FOABHC optimizes the evolutionary equation by the strategy of adding cognition,and by doing so,the potential global optimum can avoid losing the probability of being the ultimate global optimum,which results from the potential global optimum not considering its own historical track,and simply aggregating to the current optimum to make its search trajectory with many twists and turns.The value of history to individual learning in the iterative optimization process is adjusted by the linear increment dynamic variation coefficient to.The ability to break away from the local optimum and find the global optimum is greatly enhanced.The comparative experimental results show that the new algorithm has the advantages of better balance searching abilities between global and local,faster convergence speed and better convergence precision.