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

• Published in: Mathematics (Basel) Vol. 11; no. 20; p. 4390
• Main Authors: Xu, Wendi, Wang, Xianpeng, Guo, Qingxin, Song, Xiangman, Zhao, Ren, Zhao, Guodong, He, Dakuo, Xu, Te, Zhang, Ming, Yang, Yang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2023
• Subjects: Algorithms; artificial general intelligence; Artificial intelligence; Combinatorial analysis; Decomposition; Evolutionary computation; evolutionary transfer optimization; Genetic algorithms; green scheduling; Hypotheses; Intelligence; Job shop scheduling; Knowledge; Machine learning; Mathematical programming; Multiple objective analysis; Operators (mathematics); Optimization; Permutations; system optimization; Task scheduling; transfer learning; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11204390

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.