Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks
Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks
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DOI：
10.1109/tnnls.2016.2634548
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发表时间：
2018-03-01
影响因子：
10.4
通讯作者：
Grosan, Crina
中科院分区：
文献类型：
--
作者：
Emary, E.;Zawbaa, Hossam M.;Grosan, Crina
本文提出了灰狼优化（GWO）的一种变体，它利用强化学习原理与神经网络相结合来增强性能。目的是通过强化学习克服为算法设置正确参数的常见挑战。在GWO中，使用单个参数来控制探索/利用速率，这会影响算法的性能。我们没有使用全局的方式来改变所有代理的这个参数，而是使用强化学习来单独设置它。每个智能体的探索速率的适应取决于智能体自身的经验和搜索空间的当前地形。为了实现这一目标，基于神经网络构建经验存储库，将一组智能体的状态映射到一组专门影响探索率的相应动作。经验存储库由所有搜索代理更新，以反映经验并不断增强未来的行动。所得算法称为经验 GWO (EGWO)，其性能通过解决特征选择问题和寻找神经网络算法的最佳权重来评估。我们使用一组性能指标来评估该方法的效率。各种数据集的结果表明 EGWO 相对于原始 GWO 以及其他元启发式算法（例如遗传算法和粒子群优化）的进步。
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.