Hierarchical Decomposition based Cooperative Coevolution for Large-Scale Black-Box Optimization

Duan, Qiqi; Qu, Liang; Shao, Chang; Shi, Yuhui

10.1109/SSCI44817.2019.9003169

2019

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

2690-2697

6-9 Dec. 2019

Xiamen, China

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

Institute of Electrical and Electronics Engineers Inc.

Most state-of-the-art decomposition approaches for cooperative coevolution adopt a static graph (variable interaction matrix) viewpoint to recognize the possible separability of real-valued objective functions. Although generally they work well for partially additively separable scenarios, all of them cannot solve non-separable problems and non-additively separable problems effectively. To tackle these issues, based on our recent theoretical advance, this paper proposes a hierarchical-decomposition-based cooperative coevolutionary framework for large-scale black-box optimization (LSBBO). Specifically, the well-known cyclically random decomposition strategy is embedded in a hierarchical fashion, in order to facilitate the continuous evolution of the best-so-far solution. Such a hierarchy provides a flexible way to make a proper trade-off between computational complexity and search performance as well as exploration and exploitation. Numerical experiments on the IEEE CEC'2010 LSBBO test suite showed the complementary performance of the proposed framework (when integrated with the automatic decomposition technique).
© 2019 IEEE.

cooperative coevolution divide-and-conquer large-scale black-box optimization recursive (hierarchical) decomposition

第一

英语

EI ; CPCI-S

Guangdong Innovative and Entrepreneurial Research Team Program[2017ZT07X386] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; National Natural Science Foundation of China[61761136008]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467202109

20201108276627

Additives ; Artificial intelligence ; Economic and social effects

Artificial Intelligence:723.4 ; Chemical Agents and Basic Industrial Chemicals:803 ; Optimization Techniques:921.5 ; Social Sciences:971

EV Compendex

Southern University of Science and Technology (SUSTech), Shenzhen Key Laboratory of Computational Intelligence, Shenzhen, China

Duan, Qiqi,Qu, Liang,Shao, Chang,et al. Hierarchical Decomposition based Cooperative Coevolution for Large-Scale Black-Box Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2690-2697.