A Polynomial-Decay and Pinhole-Imaging Whale Optimization Algorithm for UAV Relay Communication Deployment

2026-06-11

AI Summary

This study addresses the non-convex, highly constrained joint optimization problem involving UAV relay positioning, altitude, transmit power, and bandwidth allocation. To tackle this challenge, the authors propose an enhanced Whale Optimization Algorithm (PWOA) that integrates uniform initialization via a Good Nodes Set, a polynomially decaying convergence factor, Pinhole Imaging-based Oppositional Learning (POBL), and an elite Gaussian local search strategy. This combination effectively balances global exploration and local exploitation while mitigating premature convergence to suboptimal solutions. Across 30 independent trials, PWOA consistently outperforms competing algorithms in terms of best, worst, mean, and standard deviation metrics, achieving mean improvements of 1.4%–18.5% and reducing standard deviations by 15%–87%, all while demonstrating the fastest convergence rate.

Abstract

Unmanned aerial vehicle (UAV) relays deliver flexible, on-demand wireless coverage, but jointly tuning the position, altitude, transmit power and bandwidth of the relay is a non-convex, heavily constrained optimization task that easily traps swarm-based optimizers in poor local optima. We propose PWOA, a Polynomial-decay and Pinhole-imaging Whale Optimization Algorithm with three complementary improvements: (i) a Good Nodes Set (GNS) initialization that spreads the initial population uniformly across the search space; (ii) a polynomial nonlinear schedule for the convergence factor that prolongs early exploration and sharpens late exploitation; and (iii) a stagnation-triggered pinhole-imaging opposition-based learning (POBL) operator paired with an elite Gaussian local search, which together escape local optima while refining the leader. On a five-dimensional UAV relay deployment problem with five inequality constraints ($N{=}30$, $T{=}500$, 30 independent runs), PWOA simultaneously attains the lowest Best, Worst, Mean and standard deviation among PWOA, WOA, SCA and IPSO, cutting the mean by $1.4$--$18.5\%$ and the standard deviation by $15$--$87\%$ over the three baselines, and exhibits the fastest average convergence.

Problem

• UAV relay deployment
• non-convex optimization
• local optima
• swarm intelligence
• constrained optimization

Innovation

• Whale Optimization Algorithm
• UAV relay deployment
• Polynomial decay
• Pinhole-imaging opposition-based learning
• Constrained optimization