Joint Power and Bandwidth Optimization for Multi-Beam HTS Systems via Hybrid PSO-GA with Grouped Constraints

This paper presents an innovative approach to joint power-bandwidth allocation in multi-beam high throughput satellite (HTS) systems, focusing on minimizing unmet system capacity (USC). We develop a novel optimization framework that integrates a hybrid meta-heuristic algorithm combining the global search capabilities of particle swarm optimization (PSO) with the evolutionary advantages of genetic algorithms (GA), and a novel grouped constraint handling mechanism with dynamic bandwidth scaling that ensures efficient resource distribution across four-color frequency reuse groups. Our methodology addresses three critical challenges in HTS resource management: non-linear coupling of power and bandwidth constraints, dynamic demand fluctuations, and inter-beam interference mitigation. Experimental results demonstrate unprecedented improvements over conventional GAIPSO methods, with our method achieving specific performance benchmarks across operational conditions: 100% USC elimination under low-load scenarios (30Gbps), 89.76% reduction efficiency at moderate loads (50Gbps), and 43.12% mitigation capability during peak traffic (70Gbps), while simultaneously exhibiting 74.4 % lower solution volatility compared to traditional optimization approaches.