果蝇算法和5种群智能算法的寻优性能研究

摘要

截止到目前为止进化式算法主要有遗传算法、蚁群算法、鱼群算法、免疫算法、粒子群算法.这些算法已经被广泛地用于寻优,但都有各自的缺点,导致其不易被用于解决实际问题.某学者提出了一种新群智能算法——果蝇算法.对该算法的起源进行分析,并将该算法与其他算法对比,通过仿真分析各个算法寻优性能.重点分析果蝇算法的寻优性能,得出果蝇算法简单、参数少、易调节、计算量小、寻优精度较高,从而较容易被用于解决实际问题,对于复杂问题算法可能不稳定.指出该算法的缺点,提出应改进的地方,对其应用前景作了概括.%The evolutionary algorithm contains the Genetic Algorithm,Ant Colony Algorithm,Fish school Algorithm,Immune Algorithm,and Particle Swarm Optimization.Those algorithms have been widely used for optimization,but all have respective shortcomings so that they are not easy to be used to solve practical problem.A new swarm intelligence algorithm——A Fruit Fly Optimization Algorithm is proposed in June 2011 by Taiwan scholars,Wen-Tsao Pan.In this paper,the origin of the algorithm is analyzed,The algorithm is compared with other algorithms,Optimizing performance of each algorithm is analyzed through simulation.Optimizing performance of the Fruit Fly Optimization Algorithm is particularly analyzed.The advantage of the Fruit Fly Optimization Algorithm is obtained:relatively simple,less parameters,easily adjust,the small amount of calculation.The optimization accuracy is high,thus it can be more easily used to solve practical problems,but algorithms may be unstable for some complex issues.Finally,shortcomings of the algorithm is pointed out,where the algorithm should be improved is proposed,and its application prospects are summarized.