基于混合策略改进的果蝇优化算法

针对基本果蝇优化算法收敛精度不高、容易陷入局部最优和收敛速度慢的问题,提出一种基于混合策略改进的果蝇优化算法(MSFOA)。受鲸鱼捕食猎物的启发,在对个体历史最优位置的更新中,采用新的组合搜索的方法,加快果蝇搜索迭代速度;在更新后的位置公式中引入自适应权重系数,提高算法的优化精度;当达到局部收敛状态时,结合多尺度高斯变异算子解决局部最优的限制。采用6个测试函数的仿真结果表明,MSFOA算法相比其它算法具有更快的收敛速度和较高的寻优精度。

Mixed strategy based improved fruit fly optimization algorithm

Aiming at the problems that the basic fruit fly optimization algorithm has low convergence precision and convergence speed,and that it is easy to fall into local optimum,a fruit fly optimization algorithm based on hybrid strategy(MSFOA)was proposed.Inspired by the whale prey predators,in the update of the individual’s historical optimal position,the new combined search method was adopted to accelerate the speed of fruit fly search iteration.The adaptive weight coefficient was introduced in the updated position formula to improve the algorithm optimization accuracy.When reaching the local convergence state,the multi-scale Gaussian mutation operator was combined to solve the local optimal limitation.Simulation results of six test functions show that the proposed MSFOA algorithm has higher convergence speed and better precision than other algorithms.