| Summary: | For the optimization analysis of pavement maintenance programs, combinatorial optimization is a pervasive problem. Genetic algorithms (GAs) are widely used to solve combinatorial optimization problems in pavement maintenance programs. However, owing to the stochastic search mechanisms underlying GAs, they are more likely to produce a relatively unsatisfactory solution due to premature convergence. Hence, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem. To the best of our knowledge, this is the first time that a BCS algorithm has been applied to pavement maintenance management system. Three hypothetical cases are used to investigate and demonstrate the effectiveness of the BCS algorithm, in which uncertainty-based performance degradation is considered. The results of a comparison between GA and BCS clearly justify the advantages of the search paths underlying the BCS in alleviating premature convergence. Therefore, the BCS algorithm can help decision makers to make more appropriate trade-off decisions for pavement maintenance programs.
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