Q-learning and hyper-heuristic based algorithm recommendation for changing environments
A considerable amount of research has been devoted to solving static optimization problems via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all problem-related data remain unchanged during the optimization process, which is not a realistic assumption. Recently,...
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| Vydané v: | Engineering applications of artificial intelligence Ročník 102; s. 104284 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.06.2021
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| Predmet: | |
| ISSN: | 0952-1976, 1873-6769 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | A considerable amount of research has been devoted to solving static optimization problems via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all problem-related data remain unchanged during the optimization process, which is not a realistic assumption. Recently, dynamic optimization problems (DOPs) grabbed remarkable attention from the research community. However, the literature still lacks clear guidelines on selecting the most appropriate bio-inspired algorithm under changing environments. Due to the availability of many design choices, the selection of a suitable bio-inspired metaheuristic algorithm becomes an immediate challenge. This study proposes an algorithm recommendation architecture using Q-learning and hyper-heuristic approaches to help decision-makers select the most suitable bio-inspired algorithm for a given problem. To this end, Artificial Bee Colony (ABC), Manta Ray Foraging Optimization (MRFO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA) are employed as low-level optimizers so that the Q-learning and hyper-heuristic automatically select the optimizer in each cycle of the optimization process. The proposed algorithms are implemented in dynamic multidimensional knapsack problems, a natural extension of the well-known 0–1 knapsack problem. The performances of the recommender and standalone bio-inspired algorithms are evaluated through a comprehensive experimental analysis including appropriate statistical tests. Thus, the significant differences among the algorithms are revealed. The obtained results point out the efficiencies of the Q-learning-based algorithm recommender and MRFO in solving the dynamic multidimensional knapsack problem.
•Algorithm recommendation via Q-learning and hyper-heuristic is proposed.•Four bio-inspired metaheuristic algorithms are used as low-level optimizers.•Dynamic multidimensional knapsack problems are solved.•Statistical verification is provided. |
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| ISSN: | 0952-1976 1873-6769 |
| DOI: | 10.1016/j.engappai.2021.104284 |