An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization

Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence Vol. 4; no. 3; pp. 369 - 384
Main Authors: Chen, Yongliang, Zhong, Jinghui, Feng, Liang, Zhang, Jun
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
adaptive strategy
Archives & records
Artificial intelligence
Crossovers
dynamic control
Evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Knowledge management
Knowledge transfer
many-task optimization
multi-task optimization
Multiple objective analysis
Optimization
Similarity
Sociology
Statistics
Task analysis
ISSN:2471-285X, 2471-285X
Online Access:Get full text
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Summary:Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select suitable "assisted" task for a given task by considering the similarity between tasks and the accumulated rewards of knowledge transfer during the evolution. Besides, a knowledge transfer schema via crossover is adopted to exchange information among tasks to improve the search efficiency. In addition, to facilitate measuring similarity between tasks and transferring knowledge among tasks that arrive at different time instances, multiple archives are integrated with the proposed MaTEA. Experiments on both single-objective and multi-objective optimization problems have demonstrated that the proposed MaTEA can outperform the state-of-the-art multi-task evolutionary algorithms, in terms of search efficiency and solution accuracy. Besides, the proposed MaTEA is also capable of solving dynamic many-task optimization where tasks arrive at different time instances.
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2019.2916051