Many-objective optimization is a critical research topic in the evolutionary computing community. Many algorithms have been proposed to tackle this problem, with evolutionary algorithms based on the hypervolume being among the most effective ones. However, calculating the hypervolume indicator in high-dimensional objective spaces remains time-consuming. To address this issue, we propose a two-stage hypervolume-based evolutionary algorithm (ToSHV) that separates global search and local search to ensure both convergence and diversity. ToSHV performs a global search in the first stage by generating multiple offspring per generation. We modified the R2HCA method to estimate the overall hypervolume contribution, avoiding the time-consuming nature of updating the hypervolume contribution with the greedy method. In the second stage, only one offspring is produced per generation to emphasize local exploration and enhance population diversity. Furthermore, a stage-switching mechanism is designed to dynamically select the appropriate search mode based on the prevailing population distribution. We evaluate our algorithm on WFG and DTLZ test suites, comparing it with three hypervolume-based algorithms and four state-of-the-art algorithms. Experimental results show that our approach is competitive in most cases.

GNSS spoofing can progressively corrupt unmanned aerial vehicle (UAV) navigation when manipulated position solutions are fused before integrity assessment. Existing detection methods often use dead-reckoning references such as visual–inertial odometry (VIO) without fully accounting for accumulated drift or GNSS-dependent global alignment. This paper presents a closed-loop software-in-the-loop (SITL) evaluation platform and a detection-aware delayed multi-sensor fusion framework for UAV GNSS spoofing mitigation. The platform couples state estimation, flight control, vehicle dynamics, and sensor feedback to enable Monte Carlo evaluation under realistic navigation–control interactions. A cumulative sum (CUSUM) detector monitors consistency between GNSS and drift-compensated VIO, while recent GNSS factors are quarantined and incorporated into a factor graph only after integrity screening. Experiments quantify false-alarm rate, detection probability, and latency across detector settings and spoofing deviation rates. Onboard implementation and controlled injection experiments further assess real-time feasibility and hardware transferability. Under the evaluated sensor conditions and gradual-drift attack model, a 10–15 s window yielded detection probabilities exceeding 96% for attacks with a deviation rate of 0.5 m/s, with an approximately 3% false-alarm rate. These findings demonstrate the framework’s potential for spoofing mitigation and the platform’s utility for closed-loop evaluation.