Paper Info
Title
Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks
Abstract
Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT. The multifactorial evolutionary algorithm (MFEA) represents one of the most widely used implementation paradigms of EMT. However, it tends to suffer from noneffective or even negative knowledge transfer. To address this issue and improve the performance of MFEA, we incorporate a prior-knowledge-based multiobjectivization via decomposition (MVD) into MFEA to construct strongly related meme helper-tasks. In the proposed method, MVD creates a related multiobjective optimization problem for each component task based on the corresponding problem structure or decision variable grouping to enhance positive intertask knowledge transfer. MVD can reduce the number of local optima and increase population diversity. Comparative experiments on the widely used test problems demonstrate that the constructed meme helper-tasks can utilize the prior knowledge of the target problems to improve the performance of MFEA.
Year
DOI
Venue
2022
10.1109/TCYB.2021.3050516
IEEE Transactions on Cybernetics
Keywords
DocType
Volume
Algorithms,Biological Evolution,Computer Simulation
Journal
52
Issue
ISSN
Citations 
8
2168-2267
0
PageRank 
References 
Authors
0.34
47
8
Name
Order
Citations
PageRank
Xiaoliang Ma118218.51
Jian Yin200.34
Anmin Zhu300.34
Xiaodong Li4156084.64
Yanan Yu571.07
Lei Wang614137.05
Yutao Qi7817.35
Zexuan Zhu898957.41