A collaborative neurodynamic optimization algorithm to traveling salesman problem

This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with n × n neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the...

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Bibliographic Details
Published inComplex & intelligent systems Vol. 9; no. 2; pp. 1809 - 1821
Main Authors Zhong, Jing, Feng, Yuelei, Tang, Shuyu, Xiong, Jiang, Dai, Xiangguang, Zhang, Nian
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.04.2023
Springer Nature B.V
Springer
Subjects
Algorithms
Collaboration
Collaborative neurodynamic optimization
Combinatorial optimization problems
Complexity
Computational Intelligence
Convergence
Data Structures and Information Theory
Engineering
Hopfield neural network
Intelligent systems
Linear programming
Neural networks
Neurons
Optimization algorithms
Original Article
Particle swarm optimization
Traveling salesman problem
Velocity
Collaborative neurodynamic optimization
Traveling salesman problem
Combinatorial optimization problems
Hopfield neural network
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Summary:This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with n × n neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the convergence condition of CHNN and solve TSP by CHNN. Finally, a population of CHNNs is used to search for local optimal solutions of TSP and the globally optimal solution is obtained using particle swarm optimization. Experimental results show the effectiveness of the CNO approach for solving TSP.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-022-00884-6