An artificial neural network for solving quadratic zero-one programming problems

Ranjbar, Mahdi; Effati, Sohrab; Miri, S.M.

doi:10.1016/j.neucom.2016.12.064

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Fischer-Burmeister NCP-function and the neural network 9, is applied to solve this problem. The optimal solution is x = (0, 0, 1, 1, 0, 0, 0, 1, 0) [37] and according to Figure 8(a), it can be seen that the proposed model is convergent to the optimal solution. x ni , The amount of i th resource allocated to the n th factory, y nj…”

Section: Illustrative Examplesmentioning

confidence: 95%

A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

Ezazipour¹,

Golbabai²

2020

Kybernetika

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Section: Illustrative Examplesmentioning

confidence: 95%

A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

Ezazipour¹,

Golbabai²

2020

Kybernetika

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“…The pioneering works on a RNN for constrained programming problem are achieved by Hopfield and Tank 28,29 . There are several popular RNN's for constrained optimization which can be divided into the following two classes: First, the gradient based RNN's in References 30–41. Second, the projection type RNN's in References 42–59.…”

Section: Introductionmentioning

confidence: 99%

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Jahangiri,

Nazemi

2024

Adaptive Control & Signal

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SummaryIn the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.

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“…For the past decades, substantial efforts have been devoted to exploring brand new computing model, for example, quantum turing machine (QTM) [ 10–12 ] , artificial neural network (ANN) [ 13–15 ] , and biologically inspired computing (BIC) [ 16–20 ] , etc. Research on TM gave birth to modern general purpose electronic computer, and left up‐till‐now unsolved problem in complexity theory whether P=NP.…”

Section: Introductionmentioning

confidence: 99%

Probe Machine Based Computing Model for Maximum Clique Problem

Chen

Yin

Zhen

et al. 2022

Chinese J of Electronics

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Probe machine (PM) is a recently reported mathematic model with massive parallelism. Herein, we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently, we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP‐complete problem is generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. The work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP‐complete problem.

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An artificial neural network for solving quadratic zero-one programming problems

Cited by 10 publications

References 26 publications

A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Probe Machine Based Computing Model for Maximum Clique Problem

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