Robust Adaptive Particle Swarm Optimization for High-Level Waypoint Planning in UAV-Swarm Active Source Seeking

Active signal-source seeking by swarms of unmanned aerial vehicles (UAVs) requires reliable waypoint decisions under measurement corruption, misleading response peaks, and geometric constraints. We propose MI-PSO-Adaptive, a high-level waypoint planning framework based on particle swarm optimization (PSO). The framework combines repeated sampling with robust aggregation, adaptive exploration with archive-based social target selection, candidate waypoint guidance, and geometric constraint repair. We evaluated the framework in a three-dimensional simulation using a five-sample measurement budget, waypoint limits, obstacles, and reproducible random seed protocols. In the default unimodal Gaussian field, MI-PSO-Adaptive achieved a mean localization error of 35.36 m and a success rate of 94%; model-matched Gaussian estimation baselines remained more accurate. In the fixed Gaussian mixture field, it achieved a mean localization error of 54.03 m and a success rate of 66%. In contrast, the other evaluated optimization methods and unimodal-model estimation baselines failed to reach the target region. Under 5% outlier contamination, robust aggregation achieved a mean localization error of 157.80 m and a success rate of 52%, outperforming arithmetic mean MI-PSO, although the error distribution remained heavy tailed. Functional ablation showed that adaptive exploration with social target selection helped the swarm escape misleading interference peaks, whereas candidate waypoint guidance further reduced localization errors after effective exploration. The method is therefore intended for discrete high-level waypoint planning under the tested model-mismatch conditions rather than serving as a general replacement for model-based estimation or a guarantee of low-level flight feasibility.