The cooperation of agents in smart grids to form coalitions could bring benefit both for agent itself and the distribution power system. To tackle the problem as a game of partition form function poses significant computing challenges due to the huge search space for the optimization problem. In this paper, we propose a stochastic optimization approach using Population Based Incremental Learning (PBIL) algorithm with top-k Merit Weighting and a customized strategy for choosing the initial probability to solve the problem. Empirical results show that the proposed algorithm gives competitive performance compared with a few stochastic optimization algorithms.
CITATION STYLE
Lee, S. H. S., Deng, J. D., Peng, L., Purvis, M. K., & Purvis, M. (2017). Top-k merit weighting PBIL for optimal coalition structure generation of smart grids. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10637 LNCS, pp. 171–181). Springer Verlag. https://doi.org/10.1007/978-3-319-70093-9_18
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