Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence

Wendi Xu, Xianpeng Wang, Qingxin Guo (), Xiangman Song, Ren Zhao, Guodong Zhao, Dakuo He (), Te Xu, Ming Zhang and Yang Yang ()
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Wendi Xu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xianpeng Wang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Qingxin Guo: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xiangman Song: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ren Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Guodong Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Dakuo He: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Te Xu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ming Zhang: Key Laboratory for Radio Astronomy, Chinese Academy of Sciences, Nanjing 210000, China
Yang Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 20, 1-11

Abstract: 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.

Keywords: evolutionary transfer optimization; green scheduling; transfer learning; artificial general intelligence; mathematical programming; system optimization; carbon neutrality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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