Fast multiobjective immune optimization approach solving multiobjective interval number programming
As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background,...
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| Veröffentlicht in: | Swarm and evolutionary computation Jg. 51; S. 100578 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.12.2019
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| ISSN: | 2210-6502 |
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| Abstract | As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background, it is still open, and further studies are needed on basic theory, model transformation and intelligent optimizers. Therein, this work not only gropes a new shortcut to tackling one such model, but also proposes a novel multiobjective interval number immune optimization algorithm. The intrinsic solution relation between the model and a related natural interval extension one is discovered in terms of the new concept of optimal-value vector solution, by which a fast interval nondominated sorting approach is acquired. The algorithm mainly consists of population division, proliferation, evolution, selection and memory update, in which a co-evolutionary mechanism is designed to promote the current population to move quickly towards the Pareto front with the assistance of the sorting approach and an external archive set. The algorithm's resource consumption depends mainly on the archive's size. Comparative experiments have validated that the optimizer can effectively perform well over the compared approaches and is significantly superior to them with regard to efficiency. |
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| AbstractList | As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background, it is still open, and further studies are needed on basic theory, model transformation and intelligent optimizers. Therein, this work not only gropes a new shortcut to tackling one such model, but also proposes a novel multiobjective interval number immune optimization algorithm. The intrinsic solution relation between the model and a related natural interval extension one is discovered in terms of the new concept of optimal-value vector solution, by which a fast interval nondominated sorting approach is acquired. The algorithm mainly consists of population division, proliferation, evolution, selection and memory update, in which a co-evolutionary mechanism is designed to promote the current population to move quickly towards the Pareto front with the assistance of the sorting approach and an external archive set. The algorithm's resource consumption depends mainly on the archive's size. Comparative experiments have validated that the optimizer can effectively perform well over the compared approaches and is significantly superior to them with regard to efficiency. |
| ArticleNumber | 100578 |
| Author | Zhang, Zhuhong |
| Author_xml | – sequence: 1 givenname: Zhuhong surname: Zhang fullname: Zhang, Zhuhong email: zhzhang@gzu.edu.cn organization: Department of Big Data Science & Engineering, College of Big Data & Information Engineering, Guizhou University, Guiyang, Guizhou, 550025, China |
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| CitedBy_id | crossref_primary_10_1007_s00500_020_05165_3 crossref_primary_10_1016_j_swevo_2025_102153 crossref_primary_10_1016_j_icheatmasstransfer_2020_104867 crossref_primary_10_1007_s10845_022_01935_y crossref_primary_10_1155_2021_9031448 crossref_primary_10_1016_j_cor_2021_105367 crossref_primary_10_1016_j_energy_2020_119014 crossref_primary_10_1007_s00500_020_05392_8 crossref_primary_10_1016_j_eswa_2023_122751 crossref_primary_10_1016_j_energy_2020_117739 crossref_primary_10_1016_j_knosys_2020_105781 |
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| Keywords | Natural interval extension programming Nondominated sorting Multiobjective interval number programming Optimal-value vector solution Multiobjective immune optimization |
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