Abstract
This chapter presents a hybrid meta-heuristic method which combines particle swarm optimization (PSO) and genetic algorithm (GA) search procedures to predict the pavement condition index (PCI) based on Surveyed Inspection Units (SIUs). Both PSO and GA are used and a comparison is made among three approaches for evaluating the optimal arrangement of SIUs. A hybrid method was developed to build and optimize the models. The performances of these hybrid models were compared based on Sampling Error (SE), Total Network Inspection Error (TNIE), inspection time based on CPU time (seconds), total number of SIUs, and others. Based on the results of the computational experiments, one of the proposed heuristic procedures is used for solving problems in the arrangement of surveyed asphalt pavement inspection units. The study reveals that the hybrid model outperforms both the PSO and the GA based models.
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