期刊
NEURAL NETWORKS
卷 117, 期 -, 页码 191-200出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.05.010
关键词
Graph partitioning; Neural network; Combinatorial optimization; NP-hard problem; Deterministic annealing neural network algorithm
资金
- NSFC, China [61803279, 71471091, 71771161, 11871366, 51874205, 61876121, 61672371]
- Hong Kong SAR Government [CityU 11302715]
- Ministry of Housing and Urban and Rural Construction [2018-K1-007]
- Eastern Scholar Program at Shanghai Institutions of Higher Learning, Foundation of Key Laboratory in Science and Technology Development Project of Suzhou [SZS201813, SZS201609]
- Key Research & Development Plan of Jiangsu Province [BE2017663]
- China Scholarship Council, China
Graph partitioning, a classical NP-hard combinatorial optimization problem, is widely applied to industrial or management problems. In this study, an approximated solution of the graph partitioning problem is obtained by using a deterministic annealing neural network algorithm. The algorithm is a continuation method that attempts to obtain a high-quality solution by following a path of minimum points of a barrier problem as the barrier parameter is reduced from a sufficiently large positive number to 0. With the barrier parameter assumed to be any positive number, one minimum solution of the barrier problem can be found by the algorithm in a feasible descent direction. With a globally convergent iterative procedure, the feasible descent direction could be obtained by renewing Lagrange multipliers red. A distinctive feature of it is that the upper and lower bounds on the variables will be automatically satisfied on the condition that the step length is a value from 0 to 1. Four wellknown algorithms are compared with the proposed one on 100 test samples. Simulation results show effectiveness of the proposed algorithm. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据