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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Vydáno v:	IEEE transactions on emerging topics in computational intelligence Ročník 4; číslo 3; s. 369 - 384
Hlavní autoři:	Chen, Yongliang, Zhong, Jinghui, Feng, Liang, Zhang, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Feng, Liang Zhong, Jinghui Chen, Yongliang Zhang, Jun
Author_xml	– sequence: 1 givenname: Yongliang orcidid: 0000-0002-2483-8890 surname: Chen fullname: Chen, Yongliang email: chanwl0629@gmail.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 2 givenname: Jinghui orcidid: 0000-0003-0113-3430 surname: Zhong fullname: Zhong, Jinghui email: jinghuizhong@gmail.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Liang orcidid: 0000-0002-8356-7242 surname: Feng fullname: Feng, Liang email: liangf@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 4 givenname: Jun orcidid: 0000-0001-7835-9871 surname: Zhang fullname: Zhang, Jun email: junzhang@ieee.org organization: Victoria University, Melbourne, VIC, Australia
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SubjectTerms	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
Title	An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization
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