Modeling and Solving the Knapsack Problem with a Multi-Objective Equilibrium Optimizer Algorithm Based on Weighted Congestion Distance
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Deb, Kalyanmoy & Tiwari, Santosh, 2008. "Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1062-1087, March.
- Zouache, Djaafar & Moussaoui, Abdelouahab & Ben Abdelaziz, Fouad, 2018. "A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem," European Journal of Operational Research, Elsevier, vol. 264(1), pages 74-88.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Vahid Baradaran & Amir Hossein Hosseinian, 2020. "A bi-objective model for redundancy allocation problem in designing server farms: mathematical formulation and solution approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 935-952, October.
- Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
- Wang, Long & Wu, Jianghai & Wang, Tongguang & Han, Ran, 2020. "An optimization method based on random fork tree coding for the electrical networks of offshore wind farms," Renewable Energy, Elsevier, vol. 147(P1), pages 1340-1351.
- Jakubik, Johannes & Binding, Adrian & Feuerriegel, Stefan, 2021. "Directed particle swarm optimization with Gaussian-process-based function forecasting," European Journal of Operational Research, Elsevier, vol. 295(1), pages 157-169.
- K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
- Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
- Dan Yan & Saskia E. Werners & He Qing Huang & Fulco Ludwig, 2016. "Identifying and Assessing Robust Water Allocation Plans for Deltas Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5421-5435, November.
- K. Liagkouras & K. Metaxiotis & G. Tsihrintzis, 2022. "Incorporating environmental and social considerations into the portfolio optimization process," Annals of Operations Research, Springer, vol. 316(2), pages 1493-1518, September.
- Elias Munapo & Santosh Kumar, 2021. "Reducing the complexity of the knapsack linear integer problem by reformulation techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1087-1093, December.
- Long Wang & Jianghai Wu & Zeling Tang & Tongguang Wang, 2019. "An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms," Energies, MDPI, vol. 12(20), pages 1-16, October.
- K. Liagkouras & K. Metaxiotis, 2018. "A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem," Annals of Operations Research, Springer, vol. 267(1), pages 281-319, August.
- Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
- Audet, Charles & Bigeon, Jean & Cartier, Dominique & Le Digabel, Sébastien & Salomon, Ludovic, 2021. "Performance indicators in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 292(2), pages 397-422.
- Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
- Nour Elhouda Chalabi & Abdelouahab Attia & Khalid Abdulaziz Alnowibet & Hossam M. Zawbaa & Hatem Masri & Ali Wagdy Mohamed, 2023. "A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(14), pages 1-37, July.
- Daróczy, László & Janiga, Gábor & Thévenin, Dominique, 2014. "Systematic analysis of the heat exchanger arrangement problem using multi-objective genetic optimization," Energy, Elsevier, vol. 65(C), pages 364-373.
- Chou, Jui-Sheng & Truong, Dinh-Nhat, 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
- Djaafar Zouache & Fouad Ben Abdelaziz & Mira Lefkir & Nour El-Houda Chalabi, 2021. "Guided Moth–Flame optimiser for multi-objective optimization problems," Annals of Operations Research, Springer, vol. 296(1), pages 877-899, January.
- Hasan, Basima Hani F. & Abu Doush, Iyad & Al Maghayreh, Eslam & Alkhateeb, Faisal & Hamdan, Mohammad, 2014. "Hybridizing Harmony Search algorithm with different mutation operators for continuous problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 1166-1182.
More about this item
Keywords
MOEO_WCD; knapsack problem; multi-objective optimization; weighted crowding distance;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3538-:d:1519448. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.