Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem-Reference-Cited by-同舟云学术

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Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem

Published:2024-07-14 Issue: Volume:325 Page:187-195
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Hewa Pathiranage Ishara¹^ORCID,Neumann Frank¹^ORCID,Antipov Denis¹^ORCID,Neumann Aneta¹^ORCID

Affiliation:

1. Optimisation and Logistics, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia

Funder

Australian Research Council

Publisher

ACM

Reference45 articles.

1. Saba Sadeghi Ahouei, Jakob de Nobel, Aneta Neumann, Thomas Bäck, and Frank Neumann. 2024. Evolving reliable differentiating constraints for the chance-constrained maximum coverage problem. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM. To appear.

2. Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann, and Frank Neumann. 2020. Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In European Conference on Artificial Intelligence, ECAI 2020, Vol. 325. IOS Press, 307--314.

3. Aharon Ben-Tal, Laurent Ghaoui, and Arkadi Nemirovski. 2009. Robust optimization. Princeton University Press.

4. Abraham Charnes and William W Cooper. 1959. Chance-constrained programming. Management science 6, 1 (1959), 73--79.

5. Carlos A. Coello Coello, Gary B. Lamont, and David A. Van Veldhuizen. 2007. Evolutionary algorithms for solving multi-objective problems (second ed.). Springer.

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