Ensemble Multifactorial Evolution With Biased Skill-Factor Inheritance for Many-Task Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 27; no. 6; pp. 1735 - 1749
• Main Authors: Huynh Thi Thanh, Binh, Van Cuong, Le, Thang, Ta Bao, Long, Nguyen Hoang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive transfer; Algorithms; Evolution; Evolutionary computation; Inheritances; Knowledge management; Knowledge transfer; many-task optimization (MaTO); multifactorial evolutionary algorithm (MFEA); Multitasking; Optimization; parameter adaptation; Sociology; Statistics; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2022.3227120

Current years have witnessed an increment in the number of research activities on improving the efficacy of multitasking algorithms for tackling challenging optimization problems. However, current approaches often present two potential problems. First, although tasks may have different characteristics, existing literature usually utilizes only one search operator for all of them. Second, while multitasking environments comprise tasks of varying difficulty, previous proposals treat them equally. This article proposes an algorithm named ensemble multifactorial evolution with biased skill-factor inheritance (EME-BI) for optimizing a large number of tasks simultaneously. In EME-BI, an effective parameter adaptation based on the knowledge transfer quality with biased skill-factor inheritance mechanism is designed to minimize negative transfer and allocate generated offspring to tasks that need resources. Besides, instead of using only one fixed search operator, EME-BI can automatically select the most appropriate one for each task at each evolutionary stage. Finally, the proposed algorithm is armed with a dynamically adjusted population size to promote exploitation. Empirical studies on various many-task benchmark problems and a real-world problem are conducted to verify the efficiency of EME-BI. The results portrayed that EME-BI achieves highly competitive performance compared to several state-of-the-art algorithms regarding the solution quality, convergence trend, and computation time. This proposal also won first prize at the CEC2021 Competition on Evolutionary Multitask Optimization, multitask single-objective optimization.