具有Levy飞行特征的双子群果蝇优化算法

针对果蝇优化算法(FOA)易陷入局部最优和收敛精度不高等缺点,在果蝇算法中引入Levy飞行策略,提出了具有Levy飞行特征的双子群果蝇优化算法(LFOA).在迭代寻优过程中,根据果蝇种群的进化程度动态地将果蝇种群划分为以当代最差个体为中心的较差子群和以当代最优个体为中心的较优子群;较差子群在最优个体指导下进行全局搜索,较优子群则围绕最优个体做Levy飞行进行局部搜索,这样既平衡了种群的全局和局部搜索能力,同时又可以利用Levy飞行偶尔的长跳跃来跳出局部最优;两个子群的信息通过最优个体的改变和子群的重组进行交换.对6个典型测试函数的仿真实验表明,LFOA具有全局收敛的能力,相比FOA具有更好的收敛精度、收敛速度和收敛可靠性.

Double subgroups fruit fly optimization algorithm with characteristics of Levy flight

In order to overcome the problems of low convergence precision and easily relapsing into local optimum in Fruit fly Optimization Algorithm (FOA), by introducing the Levy flight strategy into the FOA, an improved FOA called double subgroups FOA with the characteristics of Levy flight (LFOA) was proposed. Firstly, the fruit fly group was dynamically divided into two subgroups (advanced subgroup and drawback subgroup) whose centers separately were the best individual and the worst individual in contemporary group according to its own evolutionary level. Secondly, a global search was made for drawback subgroup with the guidance of the best individual, and a finely local search was made for advanced subgroup by doing Levy flight around the best individual, so that not only both the global and local search ability balanced, but also the occasionally long distance jump of Levy flight could be used to help the fruit fly jump out of local optimum. Finally, two subgroups exchange information by updating the overall optimum and recombining the subgroups. The experiment results of 6 typical functions show that the new method has the advantages of better global searching ability, faster convergence and more precise convergence.