Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 8/2020

21-11-2018 | Original Article

A novel improved antlion optimizer algorithm and its comparative performance

Authors: Haydar Kilic, Ugur Yuzgec, Cihan Karakuzu

Published in: Neural Computing and Applications | Issue 8/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this study, the improvement of the ant lion optimization which is inspired by ant lion’s hunting strategy is dealt with. The most disadvantageous property of this algorithm is its having a long run time due to the random walking process. In order to overcome this drawback, we proposed the improved random walking model, tournament selection method instead of the roulette wheel selection method, and reproduction mechanism at the boundary values. The performance of improved ant lion optimization algorithm based on the tournament selection (IALOT) is evaluated in comparison with the commonly known and used heuristic algorithms for ten benchmark functions. Furthermore, we have tested the performance of IALOT on the training of ANFIS known as a difficult optimization problem. The benchmark and ANFIS test results show that IALOT algorithm exhibits better performance than that of the ALO algorithm.
previous article A mechanical data analysis using kurtogram and extreme learning machine
next article Optimization of green RNP problem for LTE networks using possibility theory

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Abdel-Basset M, El-Shahat D, El-Henawy I, Sangaiah AK (2018) A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making. Soft Comput 22(13):4221–4239CrossRef
2.
Abdel-Basset M, El-Shahat D, El-henawy I, Sangaiah AK, Ahmed SH (2018) A novel whale optimization algorithm for cryptanalysis in Merkle–Hellman cryptosystem. Mob Netw Appl 23(4):723–733CrossRef
3.
4.
Anand S, Afreen N, Yazdani S (2015) A novel and efficient selection method in genetic algorithm. Int J Comput Appl 129(15):7–12
5.
Babers R, Ghali NI, Hassanien AE, Madbouly NM (2015) Optimal community detection approach based on ant lion optimization. In: 2015 11th international computer engineering conference (ICENCO), pp 284–289
6.
7.
Blickle T, Thiele L (1996) A comparison of selection schemes used in evolutionary algorithms. Evol Comput 4(4):361–394CrossRef
8.
Carrano EG, Takahashi RHC, Caminhas WM, Neto OM (2008) A genetic algorithm for multiobjective training of ANFIS fuzzy networks. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp 3259–3265
9.
Cavuslu MA, Karakuzu C, Karakaya F (2012) Neural identification of dynamic systems on FPGA with improved PSO learning. Appl Soft Comput 12(9):2707–2718CrossRef
10.
Chopra N, Mehta S (2015) Multi-objective optimum generation scheduling using ant lion optimization. In: 2015 annual IEEE India conference (INDICON), pp 1–6
11.
Dorigo M, Caro GD (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2, p 1477
12.
Gupta E, Saxena A (2016) Performance evaluation of antlion optimizer based regulator in automatic generation control of interconnected power system. J Eng 2016:14
13.
Hansen N, Ros R, Schoenauer MNM, Auger A (2011) Impacts of invariance in search: when CMA-ES and PSO face ill-conditioned and non-separable problems. Appl Soft Comput 11(8):5755–5769CrossRef
14.
Hiroyasu T, Miki M, Ono Y, Minami Y (2000) Ant colony for continuous functions, vol 20. The Science and Engineering, Doshisha University, Kyoto
15.
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRef
16.
Jiang HM, Kwong CK, Ip WH, Wong TC (2012) Modeling customer satisfaction for new product development using a PSO-based ANFIS approach. Appl Soft Comput 12(2):726–734CrossRef
17.
Kamboj VK, Bhadoria A, Bath SK (2017) Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Comput Appl 28(8):2181–2192CrossRef
18.
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
19.
Karaboga D, Akay B (2007) Artificial bee colony (abc) algorithm on training artificial neural networks, pp 1–4
20.
Karaboga D, Kaya E (2013) Training ANFIS using artificial bee colony algorithm. In: IIS on IEEE (ed.) Innovations in intelligent systems and applications. IEEE, pp 1–5
21.
Karakuzu C (2010) Parameter tuning of fuzzy sliding mode controller using particle swarm optimization. Int J Innov Comput Inform Control 6:4755–4770
22.
Karakuzu C (2017) On the performance of newsworthy meta-heuristic algorithms based on point of view fuzzy modelling. Turk J Electr Eng Comput Sci 25:4706–4721CrossRef
23.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings IEEE international conference on neural networks, 1995, vol 4, pp 1942–1948
24.
25.
Luo W, Li Y (2016) Benchmarking heuristic search and optimisation algorithms in matlab. In: 22nd international conference on automation and computing (ICAC). IEEE, pp 250–255
26.
Medhane DV, Sangaiah AK (2017) Search space-based multi-objective optimization evolutionary algorithm. Comput Electr Eng 58:126–143CrossRef
27.
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83(Supplement C):80–98CrossRef
28.
Nair SS, Rana KPS, Kumar V, Chawla A (2017) Efficient modeling of linear discrete filters using ant lion optimizer. Circuits Syst Signal Process 36(4):1535–1568CrossRef
29.
Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4–27CrossRef
30.
Nischal MM, Mehta S (2015) Optimal load dispatch using ant lion optimization. Int J Eng Res Appl 5(8):10–19
31.
Nobile MS, Pasi G, Cazzaniga P, Besozzi D, Colombo R, Mauri G (2015) Proactive particles in swarm optimization: a self-tuning algorithm based on fuzzy logic. In: IEEE international conference on fuzzy systems
32.
Oussar Y, Rivals I, Dreyfus L (1998) Training wavelet networks for nonlinear dynamic input–output modeling. Neurocomputing 20:173–188CrossRef
33.
Petrovic M, Petronijevic J, Mitic M, Vukovic N, Plemic A, Miljkovic Z, Babic B (2015) The ant lion optimization algorithm for flexible process planning. JPE 18(2):65–68
34.
Raju M, Saikia LC, Sinha N (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based pid plus second order derivative controller. Int J Electr Power Energy Syst 80:52–63CrossRef
35.
Razali NM, Geraghty J et al (2011) Genetic algorithm performance with different selection strategies in solving TSP. Proc World Congr Eng 2:1134–1139
36.
Rebecca N, Shin M, Mh S, Zuriani M (2015) Ant lion optimizer for optimal reactive power dispatch solution. J Electr Syst 3:67–74
37.
Rutenbar RA (1989) Simulated annealing algorithms: an overview. IEEE Circuits Devices Mag 5(1):19–26CrossRef
38.
Sastry PS, Santharam G, Unnikrishnan KP (1994) Memory neuron networks for identification and control of dynamical systems. IEEE Trans Neural Netw 5(2):306–319CrossRef
39.
Satheeshkumar R, Shivakumar R (2016) Ant lion optimization approach for load frequency control of multi-area interconnected power systems. Circuits Syst 7(9):2357CrossRef
40.
Shoorehdeli MA, Teshnehlab M, Sedigh AK (2009) Training anfis as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter. Fuzzy Sets Syst 160(7):922–948MathSciNetCrossRef
41.
Shoorehdeli MA, Teshnehlab M, Sedigh AK, Khanesar MA (2009) Identification using anfis with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Appl Soft Comput 9(2):833–850CrossRef
42.
Srikanth K, Panwar LK, Panigrahi B, Herrera-Viedma E, Sangaiah AK, Wang GG (2018) Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem. Comput Electr Eng 70:243–260CrossRef
43.
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRef
44.
Storn R, Price K (1997) Differential evolution a simple evolution strategy for fast optimization. Dr. Dobb’s J 22(4):18–24 and 78MATH
45.
46.
Trivedi IN, Parmar SA, Bhesdadiya RH, Jangir P (2016) Voltage stability enhancement and voltage deviation minimization using ant-lion optimizer algorithm. In: 2016 2nd international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB), pp 263–267
47.
Tung NS, Chakravorty S (2016) Ant lion optimizer based approach for optimal scheduling of thermal units for small scale electrical economic power dispatch problem. Int J Grid Distrib Comput 9(7):211–224CrossRef
48.
Yao P, Wang H (2017) Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Comput 21(18):5475–5488CrossRef
49.
Zangeneh AZ, Mansouri M, Teshnehlab M, Sedigh AK (2011) Training ANFIS system with de algorithm. In Advanced computational intelligence (IWACI) fourth international workshop on IEEE, pp 308–314
Metadata
Title
A novel improved antlion optimizer algorithm and its comparative performance
Authors
Haydar Kilic
Ugur Yuzgec
Cihan Karakuzu
Publication date
21-11-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3871-9

Other articles of this Issue 8/2020

Neural Computing and Applications 8/2020 Go to the issue

Original Article

Joint motion boundary detection and CNN-based feature visualization for video object segmentation

Original Article

Fuzzy c-means and K-means clustering with genetic algorithm for identification of homogeneous regions of groundwater quality

Original Article

Imbalanced dataset-based echo state networks for anomaly detection

Review Article

Word sense disambiguation for Punjabi language using deep learning techniques

Original Article

Optimization of green RNP problem for LTE networks using possibility theory

Original Article

Data-driven historical preservation: a case study in Shanghai

Premium Partner

    Image Credits