In this paper, first, we show some of the bifurcation properties of Potts mean-fieldtheory annealing applied to traveling salesman problems. Due to these bifurcation properties, this approach, in general, produces non-optimal and non-unique solutions. As an alternative approach, we propose a nonequilibrium version of the Potts spin neural network, called Chaotic Potts Spin (CPS). CPS has several parameters, and bifurcations over each parameter are investigated. Next, experimental results are shown comparing CPS with several related approaches. CPS is good at obtaining optimal solutions for small-scale problems and semi-optimal solutions for relatively large-scale problems. We also describe a couple of CPS modifications: CPS with a heuristic...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
W pracy przedstawiono teoretyczną analizę dynamiki chaotycznych sztucznych sieci neuronowych oraz tr...
After more than a decade of research, there now exist several neural-network techniques for solving ...
We review the approaches for solving combinatorial optimization problems by chaotic dynamics. We men...
A general introduction to the use of feed-back artificial neural networks (ANN) for obtaining good a...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
In this paper, we investigate the solving ability of Hop-field neural network with iterative simulat...
A novel modified method for obtaining approximate solutions to difficult optimization problems withi...
A neural network is a model of the brain’s cognitive process, with a highly interconnected multiproc...
This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization...
Combinatorial optimization is an active field of research in Neural Networks. Since the first attemp...
This thesis discusses combinatorial optimization problems, its characteristics and solving methods. ...
This paper presents an overview of diverse topics that are seemingly different but interrelated, wit...
The Potts Neural Network approach to non-binary discrete optimizationproblems is described. It appli...
Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological appl...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
W pracy przedstawiono teoretyczną analizę dynamiki chaotycznych sztucznych sieci neuronowych oraz tr...
After more than a decade of research, there now exist several neural-network techniques for solving ...
We review the approaches for solving combinatorial optimization problems by chaotic dynamics. We men...
A general introduction to the use of feed-back artificial neural networks (ANN) for obtaining good a...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
In this paper, we investigate the solving ability of Hop-field neural network with iterative simulat...
A novel modified method for obtaining approximate solutions to difficult optimization problems withi...
A neural network is a model of the brain’s cognitive process, with a highly interconnected multiproc...
This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization...
Combinatorial optimization is an active field of research in Neural Networks. Since the first attemp...
This thesis discusses combinatorial optimization problems, its characteristics and solving methods. ...
This paper presents an overview of diverse topics that are seemingly different but interrelated, wit...
The Potts Neural Network approach to non-binary discrete optimizationproblems is described. It appli...
Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological appl...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
W pracy przedstawiono teoretyczną analizę dynamiki chaotycznych sztucznych sieci neuronowych oraz tr...
After more than a decade of research, there now exist several neural-network techniques for solving ...