Abstract
The multidemand multidimensional knapsack problem (MDMKP) is a classic NP-hard combinatorial optimization problem with a number of real-world applications. In this paper, we propose an iterated tabu search (ITS) algorithm for solving this computationally intractable problem, by integrating two solution-based tabu search procedures aiming to locally improve the solutions and a perturbation operator aiming to jump out of local optimum traps. The performance of proposed algorithm was assessed on 54 benchmark instances commonly used in the literature, and the experimental results show that the proposed algorithm is very competitive compared to the state-of-the-art algorithms in the literature. In particular, the proposed ITS algorithm improved the best known results in the literature for 27 out of 54 instances.
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Acknowledgements
This work was partially supported by the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20170904), the National Natural Science Foundation of China (Grant No. 61703213), six talent peaks project in Jiangsu Province (Grant No. RJFW-011), and NUPTSF (Grant Nos. NY217154 and RK043YZZ18004).
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Luo, D., Lai, X., Sun, Q. (2020). Iterated Tabu Search Algorithm for the Multidemand Multidimensional Knapsack Problem. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_42
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DOI: https://doi.org/10.1007/978-981-15-5577-0_42
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