Effect of learning strategies in an evolutionary method: the case of the bi-objective quadratic multiple knapsack problem
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07555-0.pdf
Reference25 articles.
1. Aider M, Gacem O, Hifi M (2022) A hybrid population-based algorithm for the bi-objective quadratic multiple knapsack problem. Appear Exp Syst Appl 191:116238. https://doi.org/10.1016/j.eswa.2021.116238
2. Aider M, Gacem O, Hifi M (2020) A two-stage $$\varepsilon $$-constraint strategy-based heuristic for bi-objective quadratic multiple knapsack problems, In Proceedings of IEEE, 7th international conference on soft computing & machine intelligence (ISCMI). Stockholm, Sweden, pp 51-55. https://doi.org/10.1109/ISCMI51676.2020.9311581
3. Aider M, Gacem O, Hifi M (2020) Branch and solve strategies-based algorithm for the quadratic multiple knapsack problem. J Oper Res Soc. https://doi.org/10.1080/01605682.2020.1843982
4. Akbar Md-M, Rahman MS, Kaykobad M, Manning EG, Shoja GC (2006) Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls. Comput Oper Res 33(5):1259–1273
5. Akeb H, Hifi M, Ould Ahmed Mounir M-E (2011) Local branching-based algorithms for the disjunctively constrained knapsack problem. Comput Indus Eng 60:811–820
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