## 4 Related Work
### Multi-Objective Optimization  
In traditional wireless sensor networks, the most important performance parameters are typically selected as optimization objectives, with other parameters used as constraints for optimization. However, these single-objective optimization techniques often struggle to perform effectively in real network environments [33]. Therefore, the use of multi-objective optimization (MOO) strategies is more aligned with practical needs. MOO strategies consider multiple performance metrics simultaneously, such as maximum reliability, minimum delay, and maximum network lifetime. This approach achieves a better performance balance and meets the complex demands of practical applications. In heterogeneous networks, each subnetwork has different resources, mission objectives, and constraints due to the combination of various network resources. Multi-objective optimization strategies are particularly important to ensure that information reaches the desired destination safely and reliably. To achieve secure and reliable information transmission, researchers have proposed various multi-objective optimization strategies. [PERSON] et al. [34] proposed a multi-objective algorithm to address the information transmission problem when vehicles switch between different heterogeneous wireless networks while in high-speed motion. Their algorithm balances service delay and service cost of packet transmission by adding differential evolutionary variants to the multi-objective evolutionary algorithm, enhancing population diversity and promoting continuous evolution. [PERSON] et al. [35] proposed a distributed energy-efficient cluster routing based on a clustering strategy to meet the network energy demands and maintain the quality of service (QoS) for heterogeneous wireless sensor-based IoT networks. They optimized network QoS parameters such as throughput and delay using a multi-objective squid optimization algorithm to ensure the best fitness value while optimizing these parameters. [PERSON] et al. [36] proposed a multitasking and multi-objective optimization algorithm for computational offload and relay communication in an air-ground integrated network consisting of UAVs, EVUs, and GSNs. The algorithm decomposes emergency communication message transmission into two multi-objective tasks, optimizing for maximum minimum link transmission rate and minimum weighted sum of delay and energy consumption. Federated learning and Dual Deep Q Network (DDQN) jointly optimize resource allocation to improve the model's generalization performance. [PERSON] et al. [37] addressed the resource scheduling problem for device-to-device (D2D) networks with UAV clusters. They considered the number of UAVs, their locations, transmit power, flight speed, communication channels, and device assignments to maximize D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption of all UAVs. Given the mixed integer programming problem (MIPP) and NP-hard nature of the problem, they proposed a non-dominated sorting genetic algorithm-III (NSGA-III-FDU) with a flexible solution dimensionality mechanism, a discrete part generation mechanism, and an adjustment mechanism for the number of drones. [PERSON] et al. [38] discussed the use of multiple reconfigurable intelligent surface (RIS)-assisted satellite-UAV-terrestrial integrated network (IS-UAV-TN) heterogeneous network communication with co-optimization performance. They posed a multi-objective optimization problem for the collaborative UAV concerning obstacles and dynamic environments in the transmission path to maximize the system's achievable rate and minimize the UAV's energy consumption during a given mission. To facilitate online decision-making, they utilized Deep Reinforcement Learning (DRL) algorithms to achieve real-time interaction with the communication environment. [PERSON] et al.