Optimization via Rejection-Free Partial Neighbor Search
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Simulated Annealing using Metropolis steps at decreasing temperatures is widely used to solve complex combinatorial optimization problems. To improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm which avoids the inefficiency of rejections by considering all the neighbors at each step. To avoid the algorithm getting stuck in a local extreme area, we propose an enhanced version of Rejection-Free called Partial Neighbor Search, which only considers random part of the neighbors while applying the Rejection-Free technique. In this paper, we apply these methods to several examples such as quadratic unconstrained binary optimization (QUBO) problems to demonstrate superior performance of the Rejection-Free Partial Neighbor Search algorithm.
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