基于强化学习的离散层级萤火虫算法检测蛋白质复合物

蛋白质复合物的检测有助于从分子水平上理解生命的活动过程。针对群智能算法检测蛋白质复合物时假阳/阴性率高、准确率低、种群多样性下降等问题，提出了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，RLDLFA-DPC）。引入强化学习思想提出一种自适应层级划分策略，动态调整层级结构，能有效解决迭代后期种群多样性下降的问题。在层级学习策略中个体向两个优秀层级学习，避免算法陷入局部最优。为了提高蛋白质复合物检测的精度，结合个体环境信息提出自适应搜索半径的局部搜索策略。最后，在酵母蛋白质的4个数据集上，与8种经典的蛋白质复合物检测方法进行对比，验证了该方法的有效性。

Reinforcement learning-based discrete level firefly algorithm for detecting protein complexes

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 diversity 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. The effectiveness of the algorithm was verified by comparing it with eight classical protein complex detection methods on four datasets of saccharomyces cerevisiae proteins.