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 11/2019

18-05-2018 | Original Article

Hysteretic noisy frequency conversion sinusoidal chaotic neural network for traveling salesman problem

Authors: Junfei Qiao, Zhiqiang Hu, Wenjing Li

Published in: Neural Computing and Applications | Issue 11/2019

Log in

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

search-config
loading …

Abstract

This paper proposes a novel method to improve accuracy and speed for traveling salesman problem (TSP). A novel hysteretic noisy frequency conversion sinusoidal chaotic neural network (HNFCSCNN) with improved energy function is proposed for TSP to improve the solution quality and reduce the computational complexity. HNFCSCNN combines chaotic searching, stochastic wandering with hysteretic dynamics for better global searching ability. A specific activation function with two hysteretic loops in different directions is adopted to relieve the adverse impact caused by higher noise for frequency conversion sinusoidal chaotic neural network (FCSCNN). A new modified energy function for TSP which has lower computational complexity than the previous energy function is established. The simulation results show that the proposed HNFCSCNN can increase the optimization accuracy and speed of FCSCNN at higher noises, and that the proposed energy function can decrease the runtime of optimal computation. It has better optimization performance than the other several algorithms.
previous article An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets
next article A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction: an empirical assessment

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.
Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52(3):141–152MATH
2.
3.
4.
Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetMATHCrossRef
5.
Alam MN, Das B, Pant V (2015) A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination. Electr Power Syst Res 128:39–52CrossRef
6.
Abo-Hammour Z, Arqub OA, Alsmadi O et al (2014) An optimization algorithm for solving systems of singular boundary value problems. Appl Math Inf Sci 8(6):2809–2821MathSciNetCrossRef
7.
Abo-hammour Z, Alsmadi O, Momani S et al (2013) A genetic algorithm approach for prediction of linear dynamical systems. Math Probl Eng 2013(4):657–675
8.
Schoonover PL, Crossley WA, Heister SD (2015) Application of a genetic algorithm to the optimization of hybrid rockets. J Spacecr Rockets 37(5):622–629CrossRef
9.
Miao M, Wang A, Liu F (2017) Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification. Neural Comput Appl 7–8:1–15
10.
Ali ES, Elazim SMA, Abdelaziz AY (2016) Improved Harmony Algorithm and Power Loss Index for optimal locations and sizing of capacitors in radial distribution systems. Int J Electr Power Energy Syst 80:252–263CrossRef
11.
Abdelaziz AY, Ali ES, Elazim SMA (2016) Flower Pollination Algorithm and Loss Sensitivity Factors for optimal sizing and placement of capacitors in radial distribution systems. Int J Electr Power Energy Syst 78(2):207–214CrossRef
12.
Wang RL, Tang Z, Cao QP (2002) A learning method in Hopfield neural network for combinatorial optimization problem. Neurocomputing 48(1):1021–1024MATHCrossRef
13.
Guo B, Wang DH, Shen Y et al (2008) A Hopfield neural network approach for power optimization of real-time operating systems. Neural Comput Appl 17(1):11–17CrossRef
14.
Uykan Z (2013) Fast-convergent double-sigmoid Hopfield neural network as applied to optimization problems. IEEE Trans Neural Netw Learn Syst 24(6):990–996CrossRef
15.
Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877CrossRef
16.
Chen L, Aihara K (1995) Chaotic simulated annealing by a neural network model with transient chaos. Neural Netw 8(6):915–930CrossRef
17.
Wang L, Smith K (1998) On chaotic simulated annealing. IEEE Trans Neural Netw 9(4):716–718CrossRef
18.
Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl 24(3–4):723–734CrossRef
19.
Gou J, Guo WP, Wang C et al (2017) A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain. Neural Comput Appl 28(7):1–22CrossRef
20.
Hu Z, Li W, Qiao J (2017) Frequency conversion sinusoidal chaotic neural network and its application. Acta Phys Sin 66(9):090502
21.
Sun M, Zhao L, Cao W et al (2010) Novel hysteretic noisy chaotic neural network for broadcast scheduling problems in packet radio networks. IEEE Trans Neural Netw 21(9):1422–1433CrossRef
22.
Sun M, Xu Y, Dai X et al (2012) Noise-tuning-based hysteretic noisy chaotic neural network for broadcast scheduling problem in wireless multihop networks. IEEE Trans Neural Netw Learn Syst 23(12):1905–1918CrossRef
23.
Zhao L, Sun M, Cheng JH et al (2009) A novel chaotic neural network with the ability to characterize local features and its application. IEEE Trans Neural Netw 20(4):735–742CrossRef
24.
Bharitkar S, Mendel JM (2000) The hysteretic Hopfield neural network. IEEE Trans Neural Netw 11(4):879–888CrossRef
25.
Liu W, Wang L (2009) Minimizing interference in satellite communications using transiently chaotic neural networks. Comput Math Appl 57(6):1024–1029MATHCrossRef
26.
Mirzaei A, Safabakhsh R (2009) Optimal matching by the transiently chaotic neural network. Appl Soft Comput 9(3):863–873CrossRef
27.
Chen SS, Shih CW (2009) Transiently chaotic neural networks with piecewise linear output functions. Chaos, Solitons Fractals 39(2):717–730MathSciNetMATHCrossRef
28.
Zhang JH, Xu YQ (2009) Wavelet chaotic neural networks and their application to continuous function optimization. Natl Sci 1(3):204–209
29.
Xu X, Tang Z, Wang J (2005) A method to improve the transiently chaotic neural network. Neurocomputing 67:456–463CrossRef
30.
Wang L, Li S, Tian F et al (2004) A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Trans Syst Man Cybern B (Cybernetics) 34(5):2119–2125CrossRef
31.
Wang L, Shi H (2006) A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks. IEEE Trans Neural Netw 17(4):989–1000CrossRef
32.
Wang L, Liu W, Shi H (2008) Noisy chaotic neural networks with variable thresholds for the frequency assignment problem in satellite communications. IEEE Trans Syst Man Cybern C (Appl Rev) 38(2):209–217CrossRef
33.
Wang L, Liu W, Shi H (2009) Delay-constrained multicast routing using the noisy chaotic neural networks. IEEE Trans Comput 58(1):82–89MathSciNetMATHCrossRef
34.
Zhao C, Gan L (2011) Dynamic channel assignment for large-scale cellular networks using noisy chaotic neural network. IEEE Trans Neural Netw 22(2):222–232CrossRef
35.
Zhang HB, Wang XX (2011) Resource allocation for downlink OFDM system using noisy chaotic neural network. Electron Lett 47(21):1201–1202CrossRef
36.
Liu X, Xiu C (2007) A novel hysteretic chaotic neural network and its applications. Neurocomputing 70(13):2561–2565CrossRef
37.
Liu X, Xiu C (2008) Hysteresis modeling based on the hysteretic chaotic neural network. Neural Comput Appl 17(5–6):579–583CrossRef
38.
Chen SS (2011) Chaotic simulated annealing by a neural network with a variable delay: design and application. IEEE Trans Neural Netw 22(10):1557–1565CrossRef
39.
Yang G, Yi J (2014) Delayed chaotic neural network with annealing controlling for maximum clique problem. Neurocomputing 127(3):114–123CrossRef
40.
Sun M, Zhao Y, Liu Z et al (2013) Improved hysteretic noisy chaotic neural network for broadcast scheduling problem in WMNs. Telkomnika Indones J Electr Eng 11(3):596–602
41.
Sun M, Lee KY, Xu Y et al (2018) Hysteretic noisy chaotic neural networks for resource allocation in OFDMA system. IEEE Trans Neural Netw Learn Syst 29(2):273–285MathSciNetCrossRef
42.
Shuai J, Chen Z, Liu R et al (1996) Self-evolution neural model. Phys Lett A 221(5):311–316CrossRef
43.
44.
Sih GC, Tang KK (2012) Sustainable reliability of brain rhythms modeled as sinusoidal waves with frequency-amplitude trade-off. Theoret Appl Fract Mech 61:21–32CrossRef
45.
Kwok T, Smith KA (1999) A unified framework for chaotic neural-network approaches to combinatorial optimization. IEEE Trans Neural Netw 10(4):978–981CrossRef
46.
Wilson GV, Pawley GS (1988) On the stability of the travelling salesman problem algorithm of Hopfield and Tank. Biol Cybern 58(1):63–70MathSciNetMATHCrossRef
47.
Aiyer SB, Niranjan M, Fallside F (1990) A theoretical investigation into the performance of the Hopfield model. Neural Netw IEEE Trans 1(2):204–215CrossRef
48.
Sun S, Zheng J (1995) A modified algorithm and theoretical analysis for Hopfield network solving TSP. Acta Electr Sin 23(1):73–78
Metadata
Title
Hysteretic noisy frequency conversion sinusoidal chaotic neural network for traveling salesman problem
Authors
Junfei Qiao
Zhiqiang Hu
Wenjing Li
Publication date
18-05-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3535-9

Other articles of this Issue 11/2019

Neural Computing and Applications 11/2019 Go to the issue

Original Article

On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Original Article

An energy-aware cluster-based stable protocol for wireless sensor networks

Original Article

A chaotic image encryption algorithm based on 3-D bit-plane permutation

Original Article

Classification and diagnostic prediction of prostate cancer using gene expression and artificial neural networks

Original Article

Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect

Original Article

Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network

Premium Partner

    Image Credits