Beyond Performance: Designing a Super-Resolution Architecture Search Space and a Hybrid Multi-Objective Approach for Neural Architecture Optimization

Multi-objective neural architecture search (NAS) for super-resolution image restoration (SRIR) targets models that simultaneously deliver high-fidelity reconstructions and respect strict computational budgets—requirements that single-objective searches routinely overlook. In response, we introduce BASS, a versatile multi-branch search space, together with a hybrid optimisation framework that couples NSGA-III’s global exploration with fine-grained local search. The local phase is preliminarily instantiated with hill climbing, tabu search and simulated annealing, enabling a systematic comparison of their ability to refine candidate architectures. We cast the task as a tri-objective problem that maximises predicted PSNR while minimising floating-point operations and parameter count, ensuring that discovered networks remain both accurate and efficient. Extensive experiments show that hybridising NSGA-III with local search accelerates convergence and consistently yields superior architectures; the hill-climbing variant offers the best overall trade-off. The resulting BASSN models attain competitive peak signal-to-noise ratios across $\times 2$ , $\times 3$ and $\times 4$ upscaling factors while fitting diverse resource envelopes, providing a practical and scalable route toward deployable SRIR solutions.