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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 14/2020

03.10.2019 | Original Article

An improved evolution fruit fly optimization algorithm and its application

verfasst von: Xuan Yang, Weide Li, Lili Su, Yaling Wang, Ailing Yang

Erschienen in: Neural Computing and Applications | Ausgabe 14/2020

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Abstract

Fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.
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Literatur
1.
Ali ES (2015) Speed control of DC series motor supplied by photovoltaic system via firefly algorithm. Neural Comput Appl 26(6):1321–1332
2.
Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616
3.
Oshaba AS, Ali ES, Elazim SMA (2017) Pi controller design for MPPT of photovoltaic system supplying SRM via bat search algorithm. Neural Comput Appl 28(4):651–667
4.
Huo J, Liu L (2018) Application research of multi-objective artificial bee colony optimization algorithm for parameters calibration of hydrological model. Neural Comput Appl 31(9): 4715–4732
5.
6.
Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26(2):69–74
7.
Pan WT (2013) Using modified fruit fly optimisation algorithm to perform the function test and case studies. Connect Sci 25(2–3):151–160
8.
Duan Q, Mao M, Duan P, Hu B (2016) An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory. Kybernetes 45(2):210–222
9.
Jovanovic R, Tuba M, Vo S (2015) An ant colony optimization algorithm for partitioning graphs with supply and demand. Comput Sci 209(3):207–212
10.
Sharma H, Bansal JC, Arya KV (2013) Opposition based levy flight artificial bee colony. Memet Comput 5(3):1–15
11.
Chen PW, Lin WY, Huang TH, Pan WT (2013) Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Appl Math Inf Sci 7(2L):459–465
12.
Li HZ, Guo S, Li CJ, Sun JQ (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl Based Syst 37(2):378–387
13.
Sheng W, Bao Y (2013) Fruit fly optimization algorithm based fractional order fuzzy-pid controller for electronic throttle. Nonlinear Dyn 73(1–2):611–619MathSciNet
14.
Wang L, Zheng XL, Wang SY (2013) A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowl Based Syst 48(2):17–C23
15.
Pan QK, Sang HY, Duan JH, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl Based Syst 62(5):69–83
16.
Wang L, Liu R, Liu S (2016) An effective and efficient fruit fly optimization algorithm with level probability policy and its applications. Knowl Based Syst 97(C):158–174
17.
Shan D, Cao GH, Dong HJ (2013) LGMS-FOA: an improved fruit fly optimization algorithm for solving optimization problems. Math Probl Eng 2013(7):1256–1271MATH
18.
Xu F, Tao Y (2014) The improvement of fruit fly optimization algorithm. Int J Autom Comput 10(03):227–241
19.
Wu L, Xiao W, Zhang L, Liu Q, Wang J (2016) An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently. Int J Comput Intell Syst 9(1):80–90
20.
Xiao C, Hao K, Ding Y (2015) An improved fruit fly optimization algorithm inspired from cell communication mechanism. Math Probl Eng 2015:1–15
21.
Yuan X, Dai X, Zhao J, He Q (2014) On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233(3):260–271MathSciNetMATH
22.
Wang L, Shi Y, Liu S (2015) An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Syst Appl 42(9):4310–4323
23.
Tian X, Jie LI, S. O. Aeronautics, N. P. University (2017) An improved fruit fly optimization algorithm and its application in aerodynamic optimization design. Acta Aeronaut Astronaut Sin 38(4)
24.
Du TS, Ke XT, Liao JG, Shen YJ (2017) DSLC-FOA: an improved fruit fly optimization algorithm application to structural engineering design optimization problems. Appl Math Model. S0307904X17305310
25.
Darvish A, Ebrahimzadeh A (2018) Improved fruit-fly optimization algorithm and its applications in antenna arrays synthesis. IEEE Trans Antennas Propag PP(99):1–1
26.
Dorigo M, Di CG, Gambardella LM (1999) Ant algorithm for discrete optimization. Arti Life 5(2):137–172
27.
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66
28.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948
29.
Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948
30.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Kluwer, DordrechtMATH
31.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2012) Gsa: a gravitational search algorithm. Inf Sci 4(6):390–395MATH
32.
Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. Springer, Berlin
33.
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99
34.
Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design. J Mech Des 112(2):223–229
35.
Zhang C, Wang H-PB (1993) Mixed-discrete nonlinear optimization with simulated annealing. Eng Optim 21(4):277–291
36.
Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411
37.
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127
38.
Hu X, Eberhart RC, Shi Y (2003) Engineering optimization with particle swarm. In: Swarm intelligence symposium
39.
Gandomi AH, Yang X, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
40.
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(3638):3902–3933MATH
41.
Mezuramontes E, Coello CAC, Velazquezreyes J, Munozdavila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39(5):567–589MathSciNet
42.
Mezuramontes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473MathSciNetMATH
43.
Cagnina L, Esquivel SC, Coello CAC (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica (Lith Acad Sci) 32(3):319–326MATH
44.
Kaveh A, Talatahari S (2009) Engineering optimization with hybrid particle swarm and ant colony optimization. Asian J Civ Eng (Build Hous) 10(6):611–628
45.
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182MATH
46.
Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683
47.
Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014
48.
Mazhoud I, Hadjhamou K, Bigeon J, Joyeux P (2013) Particle swarm optimization for solving engineering problems: a new constraint-handling mechanism. Eng Appl Artif Intell 26(4):1263–1273
49.
Long W, Liang X, Huang Y, Chen Y (2014) An effective hybrid cuckoo search algorithm for constrained global optimization. Neural Comput Appl 25(3):911–926
50.
Liu J, Wu C, Wu G, Wang X (2015) A novel differential search algorithm and applications for structure design. Appl Math Comput 268:246–269MATH
Metadaten
Titel
An improved evolution fruit fly optimization algorithm and its application
verfasst von
Xuan Yang
Weide Li
Lili Su
Yaling Wang
Ailing Yang
Publikationsdatum
03.10.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04512-2

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