Microcanonical Mean Field Annealing: A New Algorithm for Increasing the Convergence Speed of Mean Field Annealing
نویسنده
چکیده
In this paper, we consider the convergence speed of mean field annealing (MFA). We combine MFA with microcanonical simulation(MCS) method and propose a new algorithm called microcanonical mean field annealing(MCMFA). In the proposed algorithm, cooling speed is controlled by current temperature so that computation in the MFA can be reduced without degradation of performance. In addition, the solution quality of MCMFA is not affected by the initial temperature. The properties of MCMFA are analyzed with a simple example and simulated with Hopfield neural networks(HNN). In order to compare MCMFA with MFA, both algorithms are applied to graph bipartitioning problems. Simulation results show that MCMFA produces better solution than MFA.
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