Artificial Bee Colony (ABC) algorithm which is inspired by the foraging behavior of honey bees is one of the swarm intelligence systems. This algorithm can provide the efficient exploration of the optimal solutions using three different types of the agents for optimization problems with multimodal function. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm to enhance the ability for global search using the network structure of agents. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark problems. We validate solution search performance to consider how to set the parameters.