A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process

Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.217134-217149
Main Authors: Lyu, Chao, Shi, Yuhui, Sun, Lijun
Format: Article
Language:English
Subjects:
Algorithms
Brainstorming
Brainstorming process
Clustering algorithms
Evolutionary algorithms
Evolutionary computation
evolutionary multi-task optimization
Knowledge management
Knowledge transfer
Optimization
Process control
Search problems
Storms
Task analysis
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Summary:Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world multi-task optimization problems, in recent years, some EMTO algorithms have been proposed. However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing. To tackle this problem and enrich the EMTO algorithms' family, this paper firstly designs a novel EMTO framework inspired by the brainstorming process of human beings when they solve multi-task problems. Under this framework, a novel EMTO algorithm, named as brain storm multi-task optimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed brainstorming operations. Furthermore, through investigating the knowledge transfer process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme. Finally, the proposed two algorithms are tested on benchmark problems. Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms. Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3042004