An approximation algorithm for graph partitioning via deterministic annealing neural network

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 co...

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Vydáno v:Neural networks Ročník 117; s. 191 - 200
Hlavní autoři: Wu, Zhengtian, Karimi, Hamid Reza, Dang, Chuangyin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.09.2019
Témata:
Combinatorial optimization
Deterministic annealing neural network algorithm
Graph partitioning
Neural network
NP-hard problem
Graph partitioning
Deterministic annealing neural network algorithm
NP-hard problem
Neural network
Combinatorial optimization
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí: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 well-known algorithms are compared with the proposed one on 100 test samples. Simulation results show effectiveness of the proposed algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2019.05.010