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Cuckoo Search (CS) is a meta-heuristic optimization algorithm that is inspired by breeding strategy of some cuckoo species that involves laying of eggs in the nests of other host birds. Like other population based optimization algorithms, the initial positions of the population, in the case of CS are host nests, will influence the performance of the searching. Based on this fact, we believe that the CS algorithm can further be improved by strategically selecting the starting positions of the nests instead of the standard random selection. This work suggests the use of positions generated from the Centroidal Voronoi Tessellations (CVT) as the starting points for the nests. A CVT is a Voronoi tessellation of a set such that the generators of the Voronoi sets are simultaneously their centers of mass. The CVT will initially present the problem space in equally distributed manner. The performance of CS algorithm initialized using CVT is compared with those generated from the standard CS algorithm on several benchmark test functions. The results show that the initialization of CS algorithm using the CVT improves its performance especially for benchmark functions with high-dimensional input spaces.