基于SQP局部搜索的多子群果蝇优化算法

针对基本果蝇优化算法在寻优过程中种群多样性降低导致算法易陷入早熟收敛的问题，提出了基于序列二次规划（SQP）局部搜索的多子群果蝇优化算法（MFOA-SQP）。新算法将果蝇种群均匀划分为多个子群，并引入粒子群算法中的惯性权重和学习因子，协同调节果蝇移动方向和步长；每隔一定迭代次数重新划分子群，避免种群单一化，使算法更易跳出局部最优；对子群最优个体进行SQP搜索，提高局部寻优性能。通过6个测试函数和优化广义回归神经网络对银行客户进行分类的实验结果表明，算法在寻优精度和速度方面性能优越，能够有效提高广义回归神经网络的分类准确率。

A multiple subgroups fruit fly optimization algorithm based on sequential quadratic programming local search #br#

The Fruit fly Optimization Algorithm (FOA) is easy to fall into premature convergence due to the decrease of population diversity in the process of optimization. In order to overcome the problem, a multiple subgroups fruit fly optimization algorithm based on sequential quadratic programming local search (MFOA-SQP) is proposed. The fruit flies are assigned to multiple subgroups and the mutual learning between subgroups adjusts the step by introducing the inertia weight and learning factors in particle swarm optimization algorithm. The subgroups are reclassified every a certain number of iterations, which can improve population diversity and avoid premature convergence. The best individual is searched by SQP to improve the local fruit fly depth search capability, improving the stability and accelerating the evolution of population in the late iterations. Experiments on 6 benchmark test functions are carried out and an application case that classifies banking customers by the optimized generalized regression neural network is adopted. The results show that the proposed algorithm has superior performance in terms of optimization accuracy and speed, and can effectively improve the classification accuracy of generalized regression neural networks.