Online distributed evolutionary optimization of Time Division Multiple Access protocols
作者：
Highlights：
• We propose Distributed Hill Climbing + local reinforcement to evolve TDMA protocols.
• We extensively test several reinforcement rule variants over grid or random networks.
• Different reinforcement rule assignments produce different kinds of TDMA protocols.
• Some rules find minimal-energy protocols even if energy is not explicitly optimized.
• The proposed approach is more scalable and robust than compared optimization methods.
摘要
•We propose Distributed Hill Climbing + local reinforcement to evolve TDMA protocols.•We extensively test several reinforcement rule variants over grid or random networks.•Different reinforcement rule assignments produce different kinds of TDMA protocols.•Some rules find minimal-energy protocols even if energy is not explicitly optimized.•The proposed approach is more scalable and robust than compared optimization methods.
论文关键词：Distributed evolutionary algorithm,Network protocol,Online adaptation,Time Division Multiple Access,Multi-objective optimization
论文评审过程：Received 27 April 2022, Revised 16 August 2022, Accepted 16 August 2022, Available online 27 August 2022, Version of Record 30 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118627
