Effect of Solution Information Sharing between Tasks on the Search Ability of Evolutionary Multiobjective Multitasking Algorithms

Hashimoto，Ryuichi1; Masuyama，Naoki1; Nojima，Yusuke1; Ishibuchi，Hisao2

10.1109/SSCI44817.2019.9002984

2019-12-01

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

2671-2678

6-9 Dec. 2019

Xiamen, China

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

A multitask multiobjective optimization problem (MTMOP) has multiple multiobjective problems to be solved simultaneously (i.e., multitasking of multiple multiobjective problems). One approach to such an MTMOP is evolutionary multiobjective multitasking (EMOMT). EMOMT algorithms solve multiple tasks simultaneously in a cooperative manner. They are evolutionary algorithms with multiple sub-populations. Each sub-population corresponds to a single task. During the evolutionary search, the information on each solution is shared with the sub-populations. Some EMOMT algorithms have been developed by focusing on solution information sharing. However, the effect of sharing the solution information on the search ability of EMOMT algorithms is not well examined yet. Through the examination of this effect, it is expected that better EMOMT algorithms than existing ones can be developed. In our previous study, we proposed a framework of island model-based evolutionary single-objective multitasking algorithms. Using our island model, we can analyze the effect of solution information sharing. In this paper, as an extension of our previous study, we develop an island model-based EMOMT algorithm framework for MTMOPs. Through computational experiments under various parameter settings, we examine the effect of solution information sharing on the search ability of our island model.

evolutionary multiobjective optimization information sharing island model multitask multiobjective optimization multitasking

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China[61876075] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07N386] ; Shenzhen Peacock Plan[KOTD201611.2514355531] ; Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467202107

20201108276938

Artificial intelligence ; Information analysis ; Information dissemination ; Multiobjective optimization ; Multitasking

Digital Computers and Systems:722.4 ; Artificial Intelligence:723.4 ; Information Sources and Analysis:903.1 ; Information Dissemination:903.2 ; Optimization Techniques:921.5

2-s2.0-85080935132

Scopus

1.Osaka Prefecture University,Graduate School of Engineering,Sakai, Osaka,Japan
2.Southern University of Science and Technology,Shenzhen Key Laboratory of Computational Intelligence,University Key Laboratory of Evolving,Intelligent Systems of Guangdong Province,Shenzhen,China

Hashimoto，Ryuichi,Masuyama，Naoki,Nojima，Yusuke,et al. Effect of Solution Information Sharing between Tasks on the Search Ability of Evolutionary Multiobjective Multitasking Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2671-2678.