ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper

Optimization problems, decomposition and allocation problems, graphical probabilistic model, Bayesian network, Bayesian-Dirichlet metric, Bayesian optimization algorithm, problem knowledge, parallelization, hypergraph partitioning.

The paper summarizes our recent work on the design, analysis and applications of the Bayesian optimization algorithm (BOA) and its advanced accelerated variants for solving complex - sometimes NP-complete - combinatorial optimization problems from circuit design. We review the methods for accelerating BOA for hypergraph-partitioning problem. The first method accelerates the convergence of sequential BOA by utilizing specific knowledge about the optimized problem and the second method is based on the parallel construction of a probabilistic model. In the experimental part we analyze the advantages of acceleration techniques and prove that BOA is able to solve hypergraph partitioning problems reliably, effectively, and without the need for specifying control parameters and encoding schemes as in recombination-based genetic algorithms.