A Collaborative Neurodynamic Optimization Algorithm Based on Boltzmann Machines for Solving the Traveling Salesman Problem

Hongzong Li, Jun Wang
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引用次数: 1

Abstract

The traveling salesman problem is known to be NP-hard and has numerous areas of applications. This paper proposes a collaborative neurodynamic optimization algorithm based on Boltzmann machines for solving the traveling salesman problem. A population of Boltzmann machines is employed for local search, and their initial states are repeatedly reinitialized by using the particle swarm optimization update rule for global repositioning. The efficacy of the proposed collaborative neurodynamic optimization algorithm is substantiated on four traveling salesman problem benchmark instances.
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基于Boltzmann机的旅行商问题协同神经动力学优化算法
旅行推销员问题被认为是np困难的,并且有许多领域的应用。针对旅行商问题,提出了一种基于玻尔兹曼机的协同神经动力学优化算法。利用玻尔兹曼机种群进行局部搜索,并利用粒子群优化更新规则对其初始状态进行反复初始化,实现全局重定位。通过四个旅行商问题基准实例验证了所提协同神经动力学优化算法的有效性。
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