HLWOA: An Enhanced Whale Optimization Algorithm for UAV Relay Communication Deployment

UAV relay communication deployment requires the joint optimization of relay position, altitude, transmission power, and bandwidth under capacity and deployment constraints. The problem contains nonlinear coupling among geometric location, altitude, power, and bandwidth, and the penalty-based objective forms a narrow and highly constrained search landscape. Although the whale optimization algorithm (WOA) is simple and effective for continuous optimization, its random initialization, linear control schedule, and stochastic exploitation mechanism may lead to unstable convergence on constrained deployment problems. This paper proposes HLWOA, an enhanced WOA for the studied UAV relay deployment model. HLWOA integrates low-discrepancy good-point initialization, a nonlinear control schedule, direction-preserving inertia-Assisted updating, Levy-flight perturbation, and common constraint repair to improve initial coverage, maintain search diversity, and strengthen final-stage exploitation near promising feasible or low-penalty regions. Experiments are conducted against WOA, SCA, and improved PSO (IPSO) using 30 search agents, 500 iterations, and 30 independent runs under the same computational budget and repair rule. The results show that HLWOA achieves the lowest penalized objective value, the best average performance, and the smallest variation among the compared algorithms under the adopted penalty formulation. These results suggest that the proposed search framework is effective for obtaining stable low-penalty solutions in the studied constrained UAV relay communication deployment model.