关键词: 果蝇优化算法, 群体智能, 历史认知, 收敛精度, 早熟收敛

Abstract:  Considering the problem of premature convergence in fruit fly optimization algorithm (FOA), this paper proposes a new FOA based on history cognition (FOABHC). The new algorithm FOABHC optimizes the evolutionary equation by the strategy of adding “history 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 ω. 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.

Key words: fruit fly optimization algorithm (FOA), swarm intelligence, history cognition, convergence precision, premature convergence