计算机应用研究2024，Vol.41Issue(7)：1977-1982,6.DOI:10.19734/j.issn.1001-3695.2023.11.0549
基于强化学习的离散层级萤火虫算法检测蛋白质复合物
Reinforcement learning-based discrete level firefly algorithm for detecting protein complexes
摘要
Abstract
Protein complexes play a crucial role in understanding life's molecular activity process.Aiming at the problems of high false-positive/negative rate,low accuracy,and decrease in population diversity when detecting protein complexes by swarm intelligence algorithms,this paper proposed the RLDLFA-DPC.It introduced the idea of reinforcement learning to offer an adaptive level partition strategy that dynamically adjusted the level structure,solving the issue of declining population diver-sity in the late iteration.The algorithm also incorporated a level learning strategy where individuals learn from two excellent levels to avoid falling into a local optimum.Additionally,it utilized a local search strategy with an adaptive search radius in combination with individual and environmental information to improve the accuracy of protein complex detection.Finally,the effectiveness of the algorithm was verified by comparing it with eight classical protein complex detection methods on four data-sets of saccharomyces cerevisiae proteins.关键词
蛋白质复合物/萤火虫算法/强化学习/层级学习策略/局部搜索策略Key words
protein complex/firefly algorithm/reinforcement learning/level learning strategy/local search strategy分类
信息技术与安全科学引用本文复制引用
张其文,郭欣欣..基于强化学习的离散层级萤火虫算法检测蛋白质复合物[J].计算机应用研究,2024,41(7):1977-1982,6.基金项目
国家自然科学基金资助项目(62063021,62162040) （62063021,62162040）
