Journal Article10.1016/J.ENGAPPAI.2021.104284
Q-learning and hyper-heuristic based algorithm recommendation for changing environments
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TL;DR: In this paper, an algorithm recommendation architecture using Q-learning and hyper-heuristic approaches is proposed to help decision-makers select the most suitable bio-inspired algorithm for a given problem.
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About: This article is published in Engineering Applications of Artificial Intelligence. The article was published on 01 Jun 2021. The article focuses on the topics: Metaheuristic & Knapsack problem.
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Citations
A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem
01 May 2023
TL;DR: In this article , a hyperheuristic with low-level learning (HHQL) was proposed to solve the distributed blocking flow shop scheduling problem (EEDBFSP), which considers both total tardiness (TTD) and total energy consumption (TEC).
103
Multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection for energy-aware mixed shop scheduling
01 Mar 2022
TL;DR: In this paper , a mixed-shop and flow-shop production scheduling problem with a speed-scaling policy and no-idle time strategy is formulated, and a multi-objective Q-learning-based hyper-heuristic with bi-criteria selection (QHH-BS) is developed to obtain a set of high-quality Pareto frontier solutions.
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A Cooperative Scatter Search With Reinforcement Learning Mechanism for the Distributed Permutation Flowshop Scheduling Problem With Sequence-Dependent Setup Times
TL;DR: Wang et al. as mentioned in this paper proposed a reinforcement learning mechanism (QCSS) for solving the DPFSP-SDST problem, which combines eight domain knowledge-guided perturbation operators with reinforcement learning to balance the exploration and exploitation capabilities of QCSS algorithm.
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Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
O. Alsayyed,Tareq Hamadneh,Hassan Al-Tarawneh,Mohammad Alqudah,Saikat Gochhait,Irina Leonova,Om P. Malik,Mohammad Dehghani +7 more
TL;DR: A new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO), which imitates the natural behavior of giant armadillo in the wild, and presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions.
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