Transport and logistics operated by unmanned aerial vehicles, commonly known as drones, have attracted attention in recent years for their potential to revolutionize the transport industries. For example, Amazon was the first to use drones to deliver goods. Several distribution companies have since been working on similar services. To find effective delivery routes by using vehicles and drones, a flying sidekick traveling salesman problem has firstly been formulated. This problem constructs a delivery route by using a single drone and a single vehicle. In further formulation of this problem, vehicle routing problem with drones (VRPD), in which several numbers of drones and vehicles deliver goods to customers, has been formulated. We have already proposed a solution search method based on chaotic neurodynamics for VRPD. In this article, to further achieve effective search capability for VRPD, we change neural codings of chaotic neural networks according to the combinations of neighborhood operations. Experimental results show that our proposed method exhibits better objective function values than those by the conventional chaotic search method.