Multiobjective Multitasking Optimization Based on Incremental Learning

• Published in: IEEE transactions on evolutionary computation Vol. 24; no. 5; pp. 824 - 838
• Main Authors: Lin, Jiabin, Liu, Hai-Lin, Xue, Bing, Zhang, Mengjie, Gu, Fangqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Evolutionary algorithms; Evolutionary computation; Heuristic algorithms; incremental learning; Knowledge management; Knowledge transfer; Machine learning; Mapping; Multiple objective analysis; multitask optimization; Multitasking; Optimization; Pareto optimization; Problem-solving; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2019.2962747

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms.