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

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.