Asymmetric Hopfield-type networks: theory and applications
Neural Networks
An Ant Colony Optimization Heuristic for Solving Maximum Independent Set Problems
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
New heuristic algorithm for capacitated p-median proble
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Searching for maximum cliques with ant colony optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A high order neural network to solve crossbar switch problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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Ant Colony Optimization (ACO) heuristic provides a relatively easy and direct method to handle problem's constraints (through introducing the so called solution construction process), while in the other heuristics, constraint-handling is normally sophisticated. But this makes its solving process slow for the solution construction process occupies most part of its computation time. In this paper, we propose a strategy to hybridize Hopfield discrete neural networks (HDNN) with ACO heuristic for maximum independent set (MIS) problems. Several simulation instances showed that the strategy can greatly improve ACO heuristic performance not only in time cost but also in solution quality.