A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Controlling chaos in a chaotic neural network
Neural Networks
Delay-Constrained Multicast Routing Using the Noisy Chaotic Neural Networks
IEEE Transactions on Computers
Letters: A TCNN filter algorithm to maximum clique problem
Neurocomputing
Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel chaotic neural network with the ability to characterize local features and its application
IEEE Transactions on Neural Networks
Exponential stability on stochastic neural networks with discrete interval and distributed delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On chaotic simulated annealing
IEEE Transactions on Neural Networks
Chaotic simulated annealing with decaying chaotic noise
IEEE Transactions on Neural Networks
Chaotic Simulated Annealing by a Neural Network With a Variable Delay: Design and Application
IEEE Transactions on Neural Networks
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In this paper, we propose a delayed chaotic neural network with annealing controlling strategies (DCNN-AC) to solve the NP-complete maximum clique problem (MCP). We point out some flaws in the variable delayed neural network proposed by Chen, and demonstrate that DCNN-AC is a powerful chaotic neural network through analyzing its single neural model and its ''beautiful'' chaotic dynamics. DCNN-AC has richer and more flexible chaotic dynamics and flexible annealing controlling strategies, so that it can be expected to have higher searching ability for globally optimal or near-optimal solutions. The DCNN-AC performance has been verified by simulations on some MCP benchmark instances. The comparisons with some famous proximate algorithms show the superiority of DCNN-AC in terms of the solution quality and the comparable computation time.