Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
作者：
Highlights：
• Global optimization problems are generally NP-hard problem where metaheuristic algorithms find the optimal solution in the search space.
• Reinforcement learning methods give high success rate in finding new global areas compared with metaheuristics and have a more balanced behavior.
• Three metaheuristic-reinforcement learning hybrid algorithms are proposed switching between exploration and exploitation phases as and when needed making them more successful in finding better optimized solutions.
• They have been applied over 30 benchmark functions and have been simulated to inverse kinematics of the robot arms problem.
摘要
•Global optimization problems are generally NP-hard problem where metaheuristic algorithms find the optimal solution in the search space.•Reinforcement learning methods give high success rate in finding new global areas compared with metaheuristics and have a more balanced behavior.•Three metaheuristic-reinforcement learning hybrid algorithms are proposed switching between exploration and exploitation phases as and when needed making them more successful in finding better optimized solutions.•They have been applied over 30 benchmark functions and have been simulated to inverse kinematics of the robot arms problem.
论文关键词：Metaheuristic algorithm,Reinforcement learning algorithm,Grey wolf optimization algorithm,Whale optimization algorithm,Q-learning
论文评审过程：Received 30 October 2020, Revised 11 April 2021, Accepted 12 April 2021, Available online 22 April 2021, Version of Record 29 April 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107044
