The Research Of Multiple Subgroups Fruit Fly Optimization Algorithm Based On Sequential Quadratic Programming Local Search

Because only the elite individuals are retained in the iterative optimization process,the population diversity of FOA rapidly declined in the middle and final stages,and the FOA cannot jump out of the local extremum.Aiming at this problem,a multiple subgroups fruit fly optimization algorithm based on sequential quadratic programming local search is proposed.The MFOASQP uses a multi-subgroup co-evolution strategy to divide the fruit fly population into multiple subgroups uniformly,and the inertia weight and learning factor in the particle swarm optimization are introduced to coordinate the moving direction and step length of the fruit fly.The subgroups are recalculated at regular intervals to avoid population singleness,which makes the algorithm more easily to jump out of local optimum.At the same time in order to solve the problem of FOA in local depth search,SQP local search method is introduced to the subgroup optimal individual,which strengthen local optimization performance of FOA.The performance of the algorithm is evaluated by the benchmark function,and the experimental results show that the algorithm shows better performance than FOA in many kinds of benchmark functions.