A Self-Adaptive Modified Fruit Fly Optimization Algorithm
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- 英文论文题名：A Self-Adaptive Modified Fruit Fly Optimization Algorithm
- 论文作者：Yingtong Tan ; Mei Zhang ; Jinhui Zhu ; Haiming Liu
- 英文论文作者：Yingtong Tan ; Mei Zhang ; Jinhui Zhu ; Haiming Liu ; School of Automation Science and Control Engineering ; South China University of Technology ; Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of Education ; South China University of Technology ; School of Software Engineering ; South China University of Technology
- 年：2017
- 作者机构：School of Automation Science and Control Engineering,South China University of Technology;Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of Education,South China University of Technology;School of Software Engineering,South China University of Technology;
- 英文论文关键词：Swarm intelligence optimization ; Fruit fly optimization algorithm ; Numerical optimization
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：7
- 文件大小：457k
- 原文格式：D
- 会议级别：全国
摘要
Fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm(SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.
Fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm(SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.
Fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm(SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.
引文
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[9]Meng T,Pan Q K.An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem[J].Applied Soft Computing,2017,50:79-93.
[10]Wu L,Zuo C,Zhang H.A cloud model based fruit fly optimization algorithm[J].Knowledge-Based Systems,2015,89:603-617.
[11]Wang L,Liu R,Liu S.An effective and efficient fruit fly optimization algorithm with level probability policy and its applications[J].Knowledge-Based Systems,2016,97:158-174.
[12]Dai H,Zhao G,Lu J,et al.Comment and improvement on“Anew Fruit Fly Optimization Algorithm:Taking the financial distress model as an example”[J].Knowledge-Based Systems,2014,59:159-160.
[13]Pan Q K,Sang H Y,Duan J H,et al.An improved fruit fly optimization algorithm for continuous function optimization problems[J].Knowledge-Based Systems,2014,62:69-83.
[14]Liang J J,Qin A K,Suganthan P N,et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J].IEEE transactions on evolutionary computation,2006,10(3):281-295.
[15]Pan Q K,Suganthan P N,Tasgetiren M F,et al.Aself-adaptive global best harmony search algorithm for continuous optimization problems[J].Applied Mathematics and Computation,2010,216(3):830-848.
[2]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),1996,26(1):29-41.
[3]Pan W T.A new fruit fly optimization algorithm:taking the financial distress model as an example[J].Knowledge-Based Systems,2012,26:69-74.
[4]Zhang P,Wang L.Grouped fruit-fly optimization algorithm for the no-wait lot streaming flow shop scheduling[C]//International Conference on Intelligent Computing.Springer International Publishing,2014:664-674.
[5]Li H Z,Guo S,Li C J,et al.A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm[J].Knowledge-Based Systems,2013,37:378-387.
[6]Chen P W,Lin W Y,Huang T H,et al.Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service[J].Applied Mathematics and Information Sciences,2013,7(2):459-465.
[7]Zheng X L,Wang L,Wang S Y.A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem[J].Knowledge-Based Systems,2014,57:95-103.
[8]Zhang Y,Cui G,Wu J,et al.A novel multi-scale cooperative mutation Fruit Fly Optimization Algorithm[J].Knowledge-Based Systems,2016,114:24-35.
[9]Meng T,Pan Q K.An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem[J].Applied Soft Computing,2017,50:79-93.
[10]Wu L,Zuo C,Zhang H.A cloud model based fruit fly optimization algorithm[J].Knowledge-Based Systems,2015,89:603-617.
[11]Wang L,Liu R,Liu S.An effective and efficient fruit fly optimization algorithm with level probability policy and its applications[J].Knowledge-Based Systems,2016,97:158-174.
[12]Dai H,Zhao G,Lu J,et al.Comment and improvement on“Anew Fruit Fly Optimization Algorithm:Taking the financial distress model as an example”[J].Knowledge-Based Systems,2014,59:159-160.
[13]Pan Q K,Sang H Y,Duan J H,et al.An improved fruit fly optimization algorithm for continuous function optimization problems[J].Knowledge-Based Systems,2014,62:69-83.
[14]Liang J J,Qin A K,Suganthan P N,et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J].IEEE transactions on evolutionary computation,2006,10(3):281-295.
[15]Pan Q K,Suganthan P N,Tasgetiren M F,et al.Aself-adaptive global best harmony search algorithm for continuous optimization problems[J].Applied Mathematics and Computation,2010,216(3):830-848.