Path relinking for unconstrained binary quadratic programming

This paper presents two path relinking algorithms to solve the unconstrained binary quadratic programming (UBQP) problem. One is based on a greedy strategy to generate the relinking path from the initial solution to the guiding solution and the other operates in a random way. We show extensive computational results on five sets of benchmarks, including 31 large random UBQP instances and 103 structured instances derived from the MaxCut problem. Comparisons with several state-of-theart algorithms demonstrate the efficacy of our proposed algorithms in terms of both solution quality and computational efficiency. It is noteworthy that both algorithms are able to improve the previous best known results for almost 40 percent of the 103 MaxCut instances.