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Neural Computing and Applications

, Volume 31, Issue 11, pp 7055–7069 | Cite as

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

  • Junfei Qiao
  • Zhiqiang HuEmail author
  • Wenjing Li
Original Article

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.

Keywords

Combinatorial optimization Traveling salesman problem (TSP) Hysteretic noisy frequency conversion sinusoidal chaotic neural network (HNFCSCNN) Energy function 

Notes

Acknowledgements

This work was supported by the Key Program of the National Natural Science Foundation of China (61533002), the Young Scientists Fund of the National Natural Science Foundation of China (61603009), the Beijing Science and Technology Project (Z1511000001315010) and the “Rixin Scientist” Foundation of Beijing University of Technology (2017-RX(1)-04).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.College of Mechanical and Architectural EngineeringTaishan UniversityTaianChina
  3. 3.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijingChina

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