Unmanned aerial vehicle is one of the main announced use cases of 5G/IMT2020, which is expected to have various applications in many fields. These devices have limited capabilities in terms of energy and processing. Due to the complex structure of unmanned aerial vehicle networks and the high mobility constraints, design of efficient routing protocol, for supporting such network, is a challenge. Thus, efficient routing of data among unmanned aerial vehicles between source and destination is an important issue in designing unmanned aerial vehicle networks. Proactive routing protocols are one of the main categories of routing protocols developed for mobile ad hoc networks and vehicular ad hoc networks. Optimized link state routing protocol is one of the most common proactive routing protocols that has been modified to support unmanned aerial vehicle networks, considering high mobility feature of the network. In this work, we propose a latency and energy-efficient proactive routing protocol for dense unmanned aerial vehicle networks, with high-density devices, based on optimized link state routing protocol algorithm, referred to as multi-objective optimized link state routing protocol. The proposed routing protocol is topology aware and can be used for low-latency and high-mobility applications. The proposed multi-objective optimized link state routing protocol routing algorithm considers all modified versions of optimized link state routing protocol and introduces a novel method for selecting multipoint relay nodes that considers the traffic load on the communication channel and the load on each unmanned aerial vehicle node. Moreover, the proposed algorithm considers the communication link stability and the energy constraints. The system is simulated over a reliable environment for various scenarios, and it is compared to the original optimized link state routing protocol and its modified versions. Simulation results indicate that the proposed protocol achieves higher efficiency in terms of latency, energy, and reliability. © The Author(s) 2019.