果蝇优化算法的加权策略研究

Study on the strategy of weighting in fruit fly optimization algorithm

针对基本果蝇优化算法(FOA)收敛速度慢和寻优精度不高的缺点,在位置更新公式中引入加权因子,提出了基于线性递减策略和先增后减策略的两种加权果蝇优化算法(WFOA),从而增强了种群的多样性。通过对6个测试函数的仿真实验,验证了这些策略的可行性,表明这些策略能够有效地提高算法的收敛速度和搜优精度。经过两种策略的对比,发现线性递减策略具有更快的收敛速度,而先增后减策略具有更强的鲁棒性和稍好的寻优精度。

In order to solve the problems of slow convergence rate and low convergence precision , introducing a weighting factor in the location update formula, a few weighted fruit fly optimization algorithm （WFOA）with linear decreasing strategy and first increased and then decreased strategy is presented to enhance the diversity of the population. The results on 6 test functions prove these methods are feasible, and indicate these methods can improve the convergence speed and convergence precision. By comparing the two strategies has found linear decreasing strategy faster convergence , the first increased and then decreased strategy has stronger robustness and slightly better convergence precision.