Advertisement

Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers

  • Neeraj Gupta
  • Mahdi KhosravyEmail author
  • Nilesh Patel
  • Saurabh Gupta
  • Gazal Varshney
Chapter
  • 19 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.

Keywords

Artificial neural networks (ANN) Evolutionary ANN Meta-heuristic optimization Fault detection 

References

  1. 1.
    Dey N (ed) (2017) Advancements in applied metaheuristic computing. IGI GlobalGoogle Scholar
  2. 2.
    Dey N, Ashour AS (2016) Antenna design and direction of arrival estimation in meta-heuristic paradigm: a review. Int J Serv Sci Manag Eng Technol 7(3):1–18CrossRefGoogle Scholar
  3. 3.
    Gupta N, Patel N, Tiwari BN, Khosravy M (2018) Genetic algorithm based on enhanced selection and log-scaled mutation technique. In: Proceedings of the future technologies conference, Springer, Cham, pp 730–748Google Scholar
  4. 4.
    Singh G, Gupta N, Khosravy M (2015) New crossover operators for real coded genetic algorithm (RCGA). In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS), IEEE, pp 135–140Google Scholar
  5. 5.
    Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005–2016CrossRefGoogle Scholar
  6. 6.
    Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems. Int J Adv Intell Paradig 9(5–6):464–489CrossRefGoogle Scholar
  7. 7.
    Chatterjee S, Hore S, Dey N, Chakraborty S, Ashour AS (2017) Dengue fever classification using gene expression data: a PSO based artificial neural network approach. In: Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications, Springer, Singapore, pp 331–341Google Scholar
  8. 8.
    Jagatheesan K, Anand B, Dey N, Gaber T, Hassanien AE, Kim TH (2015) A design of PI controller using stochastic particle swarm optimization in load frequency control of thermal power systems. In: 2015 fourth international conference on information science and industrial applications (ISI), IEEE, pp 25–32Google Scholar
  9. 9.
    Chakraborty S, Samanta S, Biswas D, Dey N, Chaudhuri SS (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. In: 2013 IEEE international conference on computational intelligence and computing research, pp 1–6Google Scholar
  10. 10.
    Khosravy M, Gupta N, Patel N, Senjyu T, Duque CA (2020) Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation. In: Dey N, Ashour AS, Bhattacharyya S (eds) Applied nature-inspired computing: algorithms and case studies. Springer, Singapore, pp 1–21Google Scholar
  11. 11.
    Gutierrez CE, Alsharif MR, Khosravy M, Yamashita K, Miyagi H, Villa R (2014) Main large data set features detection by a linear predictor model. In: AIP conference proceedings, vol 1618, no 1, pp 733–737Google Scholar
  12. 12.
    Khosravy M, Gupta N, Marina N, Asharif MR, Asharif F, Sethi IK (2015) Blind components processing a novel approach to array signal processing: a research orientation. In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS), IEEE, pp 20–26Google Scholar
  13. 13.
    Khosravy M, Asharif MR, Sedaaghi MH (2008) Medical image noise suppression: using mediated morphology. IEICE Tech Rep, IEICE, pp 265–270Google Scholar
  14. 14.
    Khosravy M, Asharif MR, Yamashita K (2009) A PDF-matched short-term linear predictability approach to blind source separation. Int J Innov Comput Inform Control (IJICIC) 5(11):3677–3690Google Scholar
  15. 15.
    Khosravy M, Alsharif MR, Yamashita K (2009) A PDF-matched modification to Stone’s measure of predictability for blind source separation. In: International symposium on neural networks, Springer, Berlin, Heidelberg, pp 219–228Google Scholar
  16. 16.
    Khosravy M, Asharif MR, Yamashita K (2011) A theoretical discussion on the foundation of Stone’s blind source separation. SIViP 5(3):379–388CrossRefGoogle Scholar
  17. 17.
    Khosravy M, Asharif M, Yamashita K (2008) A probabilistic short-length linear predictability approach to blind source separation. In: 23rd international technical conference on circuits/systems, computers and communications (ITC-CSCC 2008), Yamaguchi, Japan, pp 381–384Google Scholar
  18. 18.
    Khosravy M, Kakazu S, Alsharif MR, Yamashita K (2010) Multiuser data separation for short message service using ICA (信号処理). 電子情報通信学会技術研究報告. SIP, 信号処理: IEICE technical report, 109(435), pp 113–117Google Scholar
  19. 19.
    Gutierrez CE, Alsharif MR, Yamashita K, Khosravy M (2014) A tweets mining approach to detection of critical events characteristics using random forest. Int J Next-Gener Comput 5(2):167–176Google Scholar
  20. 20.
    Ashour AS, Samanta S, Dey N, Kausar N, Abdessalemkaraa WB, Hassanien AE (2015) Computed tomography image enhancement using cuckoo search: a log transform based approach. J Signal Inform Process 6(03):244CrossRefGoogle Scholar
  21. 21.
    Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Brain action inspired morphological image enhancement. Nature-inspired computing and optimization. Springer, Cham, pp 381–407CrossRefGoogle Scholar
  22. 22.
    Dey N, Mukhopadhyay S, Das A, Chaudhuri SS (2012) Analysis of P-QRS-T components modified by blind watermarking technique within the electrocardiogram signal for authentication in wireless telecardiology using DWT. Int J Image Gr Signal Process 4(7):33Google Scholar
  23. 23.
    Dey N, Ashour AS, Shi F, Fong SJ, Sherratt RS (2017) Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Trans Consum Electron 63(4):442–449Google Scholar
  24. 24.
    Sedaaghi MH, Khosravi M (2003) Morphological ECG signal preprocessing with more efficient baseline drift removal. In: Proceedings of the 7th IASTED international conference, ASC pp 205–209Google Scholar
  25. 25.
    Khosravi M, Sedaaghi MH (2004) Impulsive noise suppression of electrocardiogram signals with mediated morphological filters. In: The 11th Iranian conference on biomedical engineering, Tehran, Iran, pp 207–212Google Scholar
  26. 26.
    Khosravy M, Patel N, Gupta N, Sethi IK (2019) Image quality assessment: a review to full reference indexes. Recent trends in communication, computing, and electronics. Springer, Singapore, pp 279–288CrossRefGoogle Scholar
  27. 27.
    Hore S, Chakraborty S, Chatterjee S, Dey N, Ashour AS, Van Chung L, Le DN (2016) An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. Int J Electric Comput Eng 6(6):2088–8708Google Scholar
  28. 28.
    Sedaaghi MH, Daj R, Khosravi M (2001) Mediated morphological filters. In: Proceedings 2001 international conference on image processing (Cat. No. 01CH37205), IEEE, vol 3, pp 692–695Google Scholar
  29. 29.
    Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Morphological filters: an inspiration from natural geometrical erosion and dilation. Nature-inspired computing and optimization. Springer, Cham, pp 349–379CrossRefGoogle Scholar
  30. 30.
    Khosravy M, Punkoska N, Asharif F, Asharif MR (2014) Acoustic OFDM data embedding by reversible Walsh-Hadamard transform. In: AIP conference proceedings, vol 1618, no. 1, pp 720–723Google Scholar
  31. 31.
    Khosravy M, Alsharif MR, Guo B, Lin H, Yamashita K (2009) A robust and precise solution to permutation indeterminacy and complex scaling ambiguity in BSS-based blind MIMO-OFDM receiver. In: International conference on independent component analysis and signal separation, Springer, Berlin, Heidelberg, pp 670–677Google Scholar
  32. 32.
    Asharif F, Tamaki S, Alsharif MR, Ryu HG (2013) Performance improvement of constant modulus algorithm blind equalizer for 16 QAM modulation. Int Innov Comput Inform Control 7(4):1377–1384Google Scholar
  33. 33.
    Khosravy M, Alsharif MR, Yamashita K (2009) An efficient ICA based approach to multiuser detection in MIMO OFDM systems. Multi-carrier systems and solutions 2009. Springer, Dordrecht, pp 47–56CrossRefGoogle Scholar
  34. 34.
    Khosravy M, Alsharif MR, Khosravi M, Yamashita K (2010) An optimum pre-filter for ICA based multi-input multi-output OFDM system. In: 2010 2nd international conference on education technology and computer, IEEE, vol 5, pp V5–129Google Scholar
  35. 35.
    Picorone AAM, Oliveira TR, Sampaio-Neto R, Khosravy M, Ribeiro MV (2020) Channel characterization of low voltage electric power distribution networks for PLC applications based on measurement campaign. Int J Electric Power Energy Syst 116:105554Google Scholar
  36. 36.
    Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Perceptual adaptation of image based on Chevreul-Mach bands visual phenomenon. IEEE Signal Process Lett 24(5):594–598CrossRefGoogle Scholar
  37. 37.
    Gupta S, Khosravy M, Gupta N, Darbari H (2019) In-field failure assessment of tractor hydraulic system operation via pseudospectrum of acoustic measurements. Turk J Electric Eng Comput Sci 27(4):2718–2729Google Scholar
  38. 38.
    Gupta N, Khosravy M, Patel N, Sethi IK (2018) Evolutionary optimization based on biological evolution in plants, vol 126. Procedia Computer Science, Elsevier pp 146–155Google Scholar
  39. 39.
    Gupta N, Khosravy M, Mahela OP, Patel N (2020) Plants biology inspired genetics algorithm: superior efficiency to firefly optimizer. In: Applications of firefly algorithm and its variants, from Springer tracts in nature-inspired computing (STNIC). Springer International Publishing (in press)Google Scholar
  40. 40.
    Gupta N, Khosravy M, Patel N, Senjyu T (2018) A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access 6:48455–48477CrossRefGoogle Scholar
  41. 41.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
  42. 42.
    Xing B, Gao WJ (2014) Invasive weed optimization algorithm. Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Cham, pp 177–181CrossRefGoogle Scholar
  43. 43.
    Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154MathSciNetCrossRefGoogle Scholar
  44. 44.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des 43(3):303–315Google Scholar
  45. 45.
    Dey N, Samanta S, Yang XS, Das A, Chaudhuri SS (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio-Inspir Comput 5(5):315–326CrossRefGoogle Scholar
  46. 46.
    Moraes CA, De Oliveira, EJ, Khosravy M, Oliveira LW, Honório LM, Pinto MF (2020) A hybrid bat-inspired algorithm for power transmission expansion planning on a practical Brazilian network. In: Dey N, Ashour AS, Bhattacharyya S (eds) Applied nature-inspired computing: algorithms and case studies. Springer, Singapore, pp 71–95Google Scholar
  47. 47.
    Satapathy SC, Raja NSM, Rajinikanth V, Ashour AS, Dey N (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput Appl 29(12):1285–1307CrossRefGoogle Scholar
  48. 48.
    Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS (2019) Skin melanoma assessment using kapur’s entropy and level set—a study with bat algorithm. In: Smart intelligent computing and applications. Springer, Singapore, pp 193–202Google Scholar
  49. 49.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248CrossRefGoogle Scholar
  50. 50.
    Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18CrossRefGoogle Scholar
  51. 51.
    Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2011) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):482–500Google Scholar
  52. 52.
    Dey N, Samanta S, Chakraborty S, Das A, Chaudhuri SS, Suri JS (2014) Firefly algorithm for optimization of scaling factors during embedding of manifold medical information: an application in ophthalmology imaging. J Med Imaging Health Inform 4(3):384–394CrossRefGoogle Scholar
  53. 53.
    Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60MathSciNetCrossRefGoogle Scholar
  54. 54.
    Hrstka O, Kučerová A, Lepš M, Zeman J (2003) A competitive comparison of different types of evolutionary algorithms. Comput Struct 81(18–19):1979–1990CrossRefGoogle Scholar
  55. 55.
    Händel P, Ohlsson M, Skog I, Ohlsson J, Movelo AB (2015) Determination of activity rate of portable electronic equipment. U.S. Patent Application 14/377,689Google Scholar
  56. 56.
    Siegel S (1988) The Kruskal-Wallis one-way analysis of variance by ranks. Nonparametric statistics for the behavioral sciencesGoogle Scholar
  57. 57.
    Ostertagová E, Ostertag O, Kováč J (2014) Methodology and application of the Kruskal-Wallis test. Appl Mech Mater 611:115–120CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Neeraj Gupta
    • 1
  • Mahdi Khosravy
    • 2
    • 3
    Email author
  • Nilesh Patel
    • 1
  • Saurabh Gupta
    • 4
    • 5
  • Gazal Varshney
    • 6
  1. 1.Department of Computer Science and EngineeringOakland UniversityRochesterUSA
  2. 2.Media Integrated Communication Lab, Graduate School of EngineeringOsaka UniversitySuitaJapan
  3. 3.Electrical Engineering DepartmentFederal University of Juiz de ForaJuiz de ForaBrazil
  4. 4.Department of Advanced EngineeringJohn Deere India Pvt. Ltd.PuneIndia
  5. 5.Research Scholar, Department of Computer ScienceBanasthali VidyapithVanasthaliIndia
  6. 6.University of Information Science and TechnologyOhridNorth Macedonia

Personalised recommendations