Tabu search guided by reinforcement learning for the max-mean dispersion problem
نویسندگان
چکیده
<p style='text-indent:20px;'>We present an effective hybrid metaheuristic of integrating reinforcement learning with a tabu-search (RLTS) algorithm for solving the max–mean dispersion problem. The innovative element is to design using knowledge strategy from <inline-formula><tex-math id="M1">\begin{document}$ Q $\end{document}</tex-math></inline-formula>-learning mechanism locate promising regions when tabu search stuck in local optimum. Computational experiments on extensive benchmarks show that RLTS performs much better than state-of-the-art algorithms literature. From total 100 benchmark instances, 60 them, which ranged 500 1, 000, our proposed matched currently best lower bounds all instances. For remaining 40 or outperformed. Furthermore, additional support was applied effectiveness combined RL technique. analysis sheds light algorithm.</p>
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ژورنال
عنوان ژورنال: Journal of Industrial and Management Optimization
سال: 2021
ISSN: ['1547-5816', '1553-166X']
DOI: https://doi.org/10.3934/jimo.2020115