Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence
As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/20/4390 |
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| author | Wendi Xu Xianpeng Wang Qingxin Guo Xiangman Song Ren Zhao Guodong Zhao Dakuo He Te Xu Ming Zhang Yang Yang |
| author_facet | Wendi Xu Xianpeng Wang Qingxin Guo Xiangman Song Ren Zhao Guodong Zhao Dakuo He Te Xu Ming Zhang Yang Yang |
| author_sort | Wendi Xu |
| collection | DOAJ |
| description | As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation. |
| first_indexed | 2024-03-10T21:04:56Z |
| format | Article |
| id | doaj.art-a505e41cd96d41168772ebd976a0a809 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T21:04:56Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a505e41cd96d41168772ebd976a0a8092023-11-19T17:15:21ZengMDPI AGMathematics2227-73902023-10-011120439010.3390/math11204390Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General IntelligenceWendi Xu0Xianpeng Wang1Qingxin Guo2Xiangman Song3Ren Zhao4Guodong Zhao5Dakuo He6Te Xu7Ming Zhang8Yang Yang9College of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaKey Laboratory for Radio Astronomy, Chinese Academy of Sciences, Nanjing 210000, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaAs a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation.https://www.mdpi.com/2227-7390/11/20/4390evolutionary transfer optimizationgreen schedulingtransfer learningartificial general intelligencemathematical programmingsystem optimization |
| spellingShingle | Wendi Xu Xianpeng Wang Qingxin Guo Xiangman Song Ren Zhao Guodong Zhao Dakuo He Te Xu Ming Zhang Yang Yang Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence Mathematics evolutionary transfer optimization green scheduling transfer learning artificial general intelligence mathematical programming system optimization |
| title | Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence |
| title_full | Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence |
| title_fullStr | Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence |
| title_full_unstemmed | Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence |
| title_short | Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence |
| title_sort | decomposition is all you need single objective to multi objective optimization towards artificial general intelligence |
| topic | evolutionary transfer optimization green scheduling transfer learning artificial general intelligence mathematical programming system optimization |
| url | https://www.mdpi.com/2227-7390/11/20/4390 |
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