An Improved Differential Evolution Based Artificial Fish Swarm Algorithm and Its Application to AGV Path Planning Problems
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- 英文论文题名：An Improved Differential Evolution Based Artificial Fish Swarm Algorithm and Its Application to AGV Path Planning Problems
- 论文作者：Guangqiang Li ; Qi Liu ; Yawei Yang ; Fengqiang Zhao ; Yiran Zhou ; Chen Guo
- 英文论文作者：Guangqiang Li ; Qi Liu ; Yawei Yang ; Fengqiang Zhao ; Yiran Zhou ; Chen Guo ; School of Information Science and Technology ; Dalian Maritime University ; College of Electromechanical Engineering ; Dalian Nationalities University
- 年：2017
- 作者机构：School of Information Science and Technology,Dalian Maritime University;College of Electromechanical Engineering,Dalian Nationalities University;
- 英文论文关键词：Artificial fish swarm algorithm ; Differential evolution ; AGV ; Path planning ; Function 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;TP23
- 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：6
- 文件大小：290k
- 原文格式：D
- 会议级别：全国
摘要
AGV path planning problems play an extremely important role in navigations of AGV. Intelligence algorithms provide an effective way to solve such complicated problems. Artificial fish swarm algorithm(AFSA) is a newly proposed promising swarm intelligence optimization algorithm, yet there still exist some disadvantages of it, such as low optimization precision and convergence rate. Aiming at these defects, an improved differential evolution based artificial fish swarm algorithm(IDE-AFSA) is proposed in this paper and applied to the global path planning of AGV. Firstly, IDE-AFSA introduces the optimal positions stored in bulletin board into the preying, following and swarming behaviors of artificial fishes, which makes the population behaviors more purposeful and directional, as well as enhance the convergence speed of the proposed algorithm. Secondly, hybrid strategy is introduced, when the information on the bulletin board does not change for a certain number of iterations, operation based on differential evolution will be carried out, which helps to keep the population diversity and make proposed algorithm escape from local optima. The optimization results on the benchmark functions demonstrate that IDE-AFSA has better performance in convergence speed, optimization precision and stability compared with AFSA. Moreover, the experimental results of global path planning of AGV further verify the feasibility and validity of proposed IDE-AFSA.
AGV path planning problems play an extremely important role in navigations of AGV. Intelligence algorithms provide an effective way to solve such complicated problems. Artificial fish swarm algorithm(AFSA) is a newly proposed promising swarm intelligence optimization algorithm, yet there still exist some disadvantages of it, such as low optimization precision and convergence rate. Aiming at these defects, an improved differential evolution based artificial fish swarm algorithm(IDE-AFSA) is proposed in this paper and applied to the global path planning of AGV. Firstly, IDE-AFSA introduces the optimal positions stored in bulletin board into the preying, following and swarming behaviors of artificial fishes, which makes the population behaviors more purposeful and directional, as well as enhance the convergence speed of the proposed algorithm. Secondly, hybrid strategy is introduced, when the information on the bulletin board does not change for a certain number of iterations, operation based on differential evolution will be carried out, which helps to keep the population diversity and make proposed algorithm escape from local optima. The optimization results on the benchmark functions demonstrate that IDE-AFSA has better performance in convergence speed, optimization precision and stability compared with AFSA. Moreover, the experimental results of global path planning of AGV further verify the feasibility and validity of proposed IDE-AFSA.
AGV path planning problems play an extremely important role in navigations of AGV. Intelligence algorithms provide an effective way to solve such complicated problems. Artificial fish swarm algorithm(AFSA) is a newly proposed promising swarm intelligence optimization algorithm, yet there still exist some disadvantages of it, such as low optimization precision and convergence rate. Aiming at these defects, an improved differential evolution based artificial fish swarm algorithm(IDE-AFSA) is proposed in this paper and applied to the global path planning of AGV. Firstly, IDE-AFSA introduces the optimal positions stored in bulletin board into the preying, following and swarming behaviors of artificial fishes, which makes the population behaviors more purposeful and directional, as well as enhance the convergence speed of the proposed algorithm. Secondly, hybrid strategy is introduced, when the information on the bulletin board does not change for a certain number of iterations, operation based on differential evolution will be carried out, which helps to keep the population diversity and make proposed algorithm escape from local optima. The optimization results on the benchmark functions demonstrate that IDE-AFSA has better performance in convergence speed, optimization precision and stability compared with AFSA. Moreover, the experimental results of global path planning of AGV further verify the feasibility and validity of proposed IDE-AFSA.
引文
[1]M.Lv,T.Gao,N.Zhang,Research of AGV scheduling and path planning of automatic transport system,International Journal of Control and Automation,9(4):1-10,2016.
[2]J.Z.Huang,Y.W.Cen,A Path-Planning Algorithm for AGV Based on the Combination between Ant Colony Algorithm and Immune Regulation,Advanced Materials Research.Trans Tech Publications,2011,422:3-9.
[3]X.L.Li,A new intelligent optimization-artificial fish swarm algorithm,Ph.D Dissertation,Zhejiang University,China,2003.
[4]A.Rocha,M.Costa,E.Fernandes,A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization,Engineering Optimization,48(12):2114-2140,2016.
[5]K.Pang,W.Liu,MPPT study of solar PV power system based on improved artificial fish-swarm algorithm,Acta Energiae Solaris Sinica,35(10):2009-2014,2014.
[6]D.Sui,F.He,Image Restoration Algorithm Based on Artificial Fish Swarm Micro Decomposition of Unknown Priori Pixel,Telecommunication Computing Electronics and Control,14(1):187-194,2016.
[7]H.Wang,W.Qu,Q.Shen,Table tennis video data mining based on performance optimization of artificial fish swarm algorithm,Computer Modelling and New Technologies,18(12):584-588,2014.
[8]Z.R.Peng,Y.Zhao,H.Yin,A.Pan,Artificial Fish Swarm Algorithm Based Optimal Sensor Placement,International Journal of Control and Automation,8(4):287-300,2015.
[9]X.Yang,Q.Wang,P.Liu,Z.Peng.Structure optimization of grading and shielding devices for 750 k V composite cross-arms with muti-objective optimization method and artificial fish swarm algorithm,High Voltage Engineering,42(11):3666-3675,2016.
[10]H.Pan,G.Shi,J.Ren,Self-organizing fuzzy control based on modified artificial fish-Swarm algorithm,in Proceedings of 3rd International Conference on Instrumentation,Measurement,Computer,Communication and Control(IMCCC),2013:1562-1567.
[11]R.D.Storn,K.Price,Differential Evolution:A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces,Journal of Global Optimization,23(4):341-359,1995.
[12]S.Das,S.S.Mullick,P.N.Suganthan,Recent advances in differential evolution-An updated survey,Swarm and Evolutionary Computation,27(1):1-30,2016.
[2]J.Z.Huang,Y.W.Cen,A Path-Planning Algorithm for AGV Based on the Combination between Ant Colony Algorithm and Immune Regulation,Advanced Materials Research.Trans Tech Publications,2011,422:3-9.
[3]X.L.Li,A new intelligent optimization-artificial fish swarm algorithm,Ph.D Dissertation,Zhejiang University,China,2003.
[4]A.Rocha,M.Costa,E.Fernandes,A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization,Engineering Optimization,48(12):2114-2140,2016.
[5]K.Pang,W.Liu,MPPT study of solar PV power system based on improved artificial fish-swarm algorithm,Acta Energiae Solaris Sinica,35(10):2009-2014,2014.
[6]D.Sui,F.He,Image Restoration Algorithm Based on Artificial Fish Swarm Micro Decomposition of Unknown Priori Pixel,Telecommunication Computing Electronics and Control,14(1):187-194,2016.
[7]H.Wang,W.Qu,Q.Shen,Table tennis video data mining based on performance optimization of artificial fish swarm algorithm,Computer Modelling and New Technologies,18(12):584-588,2014.
[8]Z.R.Peng,Y.Zhao,H.Yin,A.Pan,Artificial Fish Swarm Algorithm Based Optimal Sensor Placement,International Journal of Control and Automation,8(4):287-300,2015.
[9]X.Yang,Q.Wang,P.Liu,Z.Peng.Structure optimization of grading and shielding devices for 750 k V composite cross-arms with muti-objective optimization method and artificial fish swarm algorithm,High Voltage Engineering,42(11):3666-3675,2016.
[10]H.Pan,G.Shi,J.Ren,Self-organizing fuzzy control based on modified artificial fish-Swarm algorithm,in Proceedings of 3rd International Conference on Instrumentation,Measurement,Computer,Communication and Control(IMCCC),2013:1562-1567.
[11]R.D.Storn,K.Price,Differential Evolution:A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces,Journal of Global Optimization,23(4):341-359,1995.
[12]S.Das,S.S.Mullick,P.N.Suganthan,Recent advances in differential evolution-An updated survey,Swarm and Evolutionary Computation,27(1):1-30,2016.