果蝇优化算法研究综述
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摘要
果蝇优化算法(FOA)是一种新兴的群体智能算法,其思想来源于果蝇群体觅食行为。为进一步推广应用FOA并为深入研究该算法提供相关资料,在分析FOA基本原理和优缺点的基础上,从FOA各种改进技术及其应用等方面进行深入调查,论述了该算法的改进策略,并阐述了FOA在复杂函数优化、参数优化和组合优化等方面的应用。最后对FOA发展趋势做出展望。
Fruit fly optimization algorithm( FOA) is a new group of intelligent algorithms,the idea of fruit fly from the group foraging behavior. In order to further popularize and apply FOA and provide relevant information for further study of the algorithm,based on the analysis of FOA basic principle and advantages and disadvantages,the improvement strategy of FOA from various aspects of improvement technology and its application are discussed,and the application of FOA in complex function optimization,parameter optimization and combinatorial optimization is expounded. Finally,the development trend of FOA is proposed.
Fruit fly optimization algorithm( FOA) is a new group of intelligent algorithms,the idea of fruit fly from the group foraging behavior. In order to further popularize and apply FOA and provide relevant information for further study of the algorithm,based on the analysis of FOA basic principle and advantages and disadvantages,the improvement strategy of FOA from various aspects of improvement technology and its application are discussed,and the application of FOA in complex function optimization,parameter optimization and combinatorial optimization is expounded. Finally,the development trend of FOA is proposed.
引文
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2 Paravi Torghabeh R,Khaloozadeh H.Neural networks Hammerstein Model identification based on particle swarm optimization.IEEE International Conference on Networking,Sensing and Control.New York:IEEE,2008:363—367
3 Karaboga D,Akay B.A comparative study of artificial bee colony algorithm.Applied Mathematics&Computation,2009;214(1):108 —132
4 Pan W T.A new fruit fly optimization algorithm:taking the financial distress model as an example.Knowledge-based Systems,2012;26(2):69—74
5 王林,吕盛祥,曾宇容.果蝇优化算法研究综述.控制与决策,2017;32(7);1—9Wang Lin,LüShengxiang,Zeng Yurong.Review of drosophila optimization algorithm.Control and Decision Making,2017;32(7);1—9
6吴小文,李擎.果蝇算法和5种群智能算法的寻优性能研究.火力与指挥控制,2013;(4):17—20,25Wu Xiaowen,Li Qing.Study on the optimal performance of drosophila algorithm and 5-population intelligent algorithm.Firepower and Command Control,2013;(4):17—20,25
7 臧文龙.基于改进果蝇优化算法的多目标问题探讨.统计与决策,2014;(9):51—54Zang Wenlong.Discussion on multi-objective problem based on improved drosophila optimization algorithm.Statistics and Decision,2014;(9):51—54
8 朱志同,郭星,李炜.新型果蝇优化算法的研究.计算机工程与应用,2017,53(6):40—45,59Zhu Zhitong,Guo Xing,Li Wei.Research on new fruit fly optimization algorithm.Computer Engineering&Applications,2017;53(6):40—45,59
9 Wang Y W,Zhu J M,Feng L Z,et al.Novel fruit fly optimization algorithm based on near suburb and far suburb.Computer Engineering,2017;43(2):210—214
10 Shan D,Cao G H,Dong H J.LGMS-FOA:an improved fruit fly optimization algorithm for solving optimization problems.Mathematical Problems in Engineering,2013;(7):1256—1271
11 Pan Q K,Sang H Y,Duan J H,et al.An improved fruit fly optimization algorithm for continuous function optimization problems.Knowledge-based Systems,2014;62(5):69—83
12 Yuan X,Dai X,Zhao J,et al.On a novel multi-swarm fruit fly optimization algorithm and its application.Applied Mathematics&Computation,2014;233(3):260—271
13 Liu Z X,Wang Y F,Zhang Y.Multiple population fruit fly optimization algorithm for automatic warehouse order picking operation scheduling problem.Journal of Wuhan University of Technology,2014;36(3):71—77
14 Zhang Q,Fang L,Zhao Y.Double subgroups fruit fly optimization algorithm with characteristics of Levy flight.Journal of Computer Applications,2015;35(5):1348—1352
15 Li D,Zhang W,Automation S O.Double subgroups fruit fly optimization algorithm for solving 0-1 knapsack problem.Application Research of Computers,2015;(11):3273—3277,3282
16 王雅琳,何海明,孙备,等.改进果蝇算法在净化除钴过程锌粉量优化设定中的应用.控制理论与应用,2016;(5):579 —587Wang Yalin,He Haiming,Sun Bei,et al.Application of improved drosophila algorithm in optimal setting of zinc powder in decontamination process.Control Theory and Application,2016;(5):579—587
17刘成忠,韩俊英.基于细菌迁徙的自适应果蝇优化算法.计算机工程与科学,2014;(4):690—696Liu Chengzhong,Han Junying.An adaptive fruit fly optimization algorithm based on bacterial migration.Computer Engineering and Science,2014;(4):690—696
18 张彩宏,潘广贞.融合禁忌搜索的混合果蝇优化算法.计算机工程与设计,2016;(4):907—913Zhang Caihong,Pan Guangzhen.Optimization of hybrid fruit fly optimization algorithm based on fusion tabo search.Computer Engineering and Design,2016;(4):907—913
19 韩俊英,刘成忠.自适应调整参数的果蝇优化算法.计算机工程与应用,2014;(7):50—55Han Junying,Liu Chengzhong.Optimization algorithm of fruit fly optimization with adaptive adjustment parameters.Computer Engineering and Applications,2014;(7):50—55
20 Niu J,Zhong W,Liang Y,et al.Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization.Knowledge-based Systems,2015;88(C):253—263
21 Wu L,Zuo C,Zhang H.A cloud model based fruit fly optimization algorithm.Elsevier Science Publishers B V,2015;89:603—617
22 Marko M,Najdan V,Milica P,et al.Chaotic fruit fly optimization algorithm.Knowledge-based Systems,2015;89(C):446—458
23 Pan W T.Using modified fruit fly optimisation algorithm to perform the function test and case studies.Connection Science,2013;25(2—3):151—160
24 Dai H,Zhao G,Lu J,et al.Comment and improvement on“a new fruit fly optimization algorithm:taking the financial distress model as an example”.Knowledge-based Systems,2014;59:159—160
25 Wang L,Shi Y,Liu S.An improved fruit fly optimization algorithm and its application to joint replenishment problems.Expert Systems with Applications,2015;42(9):4310—4323
26 Li J,Pan Q,Mao K,et al.Solving the steelmaking casting problem using an effective fruit fly optimization algorithm.Knowledge-based Systems,2014;72:28—36
27 Wang L,Liu R,Liu S.An effective and efficient fruit fly optimization algorithm with level probability policy and its applications.Elsevier Science Publishers B V,2016;97:158—174
28 张磊,刘成忠.一种面向多模函数改进的果蝇优化算法.计算机工程与科学,2017;(1):206—214Zhang Lei,Liu Chengzhong.A drosophila optimization algorithm for multi-mode function improvement.Computer Engineering and Science,2017;(1):206—214
29 钟伟民,牛进伟,梁毅,等.多策略果蝇优化算法及其应用.化工学报,2015;(12):4888—4894Zhong Weimin,Niu Jinwei,Liang Yi,et al.Multi-strategy drosophila optimization algorithm and its application.Journal of Chemical Industry,2015;(12):4888—4894
30 张勇,夏树发,唐冬生.果蝇优化算法对多峰函数求解性能的仿真研究.暨南大学学报,2014,35(1):82—87Zhang Yong,Xia Shufa,Tang Dongsheng.Simulation of multi-peak function based on the fly optimization algorithm.Journal of Jinan university,2014;35(1):82—87
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