Hybrid Evolutionary Search Method Based on Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Random Noise and Random Walk Algorithms
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
A methodology for low power scheduling with resources operating at multiple voltages
Integration, the VLSI Journal
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In this paper, the convergence properties of a stochastic optimization algorithm called the stochastic evolution (SE) algorithm is analyzed. We show that a generic formulation of the SE algorithm can be modeled by an ergodic Markov chain. As such, the global convergence of the SE algorithm is established as the state transition from any initial state to the globally optimal states. We propose a new criterion called the mean first visit time (MFVT) to characterize the convergence rate of the SE algorithm. With MFVT, we are able to show analytically that on average, the SE algorithm converges faster than the random search method to the globally optimal states. This result Is further confirmed using the Monte Carlo simulation