Abstract
In this paper, we incorporate a chaotic discrete Hopfield neural network (CDHNN), as a local search scheme, into a genetic particle swarm optimization (GPSO) and develop a memetic algorithm GPSO-CDHNN for the maximum cut problem. The proposed algorithm not only performs exploration by using the population-based evolutionary search ability of the GPSO, but also performs exploitation by using the CDHNN. Simulation results show that the proposed algorithm has superior ability for maximum cut problems.
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Wang, J. (2007). A Memetic Algorithm with Genetic Particle Swarm Optimization and Neural Network for Maximum Cut Problems. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_33
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DOI: https://doi.org/10.1007/978-3-540-74769-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74768-0
Online ISBN: 978-3-540-74769-7
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