Deep RL Powers Multi‑Population Evolution for Better Many‑Objective Optimization
This study introduces DQNMaOEA, a deep reinforcement learning‑guided multi‑population coevolutionary algorithm that adaptively selects sub‑populations and allocates computational resources, achieving significantly higher solution quality and up to 25% faster runtimes on benchmark and large‑scale logistics many‑objective problems compared with state‑of‑the‑art methods.
