From resampling to non-resampling: A fireworks algorithm-based framework for solving noisy optimization problems
Document Type
Conference Proceeding
Publication Date
1-1-2017
Abstract
Many resampling methods and non-resampling ones have been proposed to deal with noisy optimization problems. The former provides accurate fitness but demands more computational resources while the latter increases the diversity but may mislead the swarm. This paper proposes a fireworks algorithm (FWA) based framework to solve noisy optimization problems. It can gradually change its strategy from resampling to non-resampling during the evolutionary process. Experiments on CEC2015 benchmark functions with noises show that the algorithms based on the proposed framework outperform their original versions as well as their resampling versions.
Identifier
85026748404 (Scopus)
ISBN
[9783319618234]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/978-3-319-61824-1_53
e-ISSN
16113349
ISSN
03029743
First Page
485
Last Page
492
Volume
10385 LNCS
Grant
61272271
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Zhang, Jun Qi; Zhu, Shan Wen; and Zhou, Meng Chu, "From resampling to non-resampling: A fireworks algorithm-based framework for solving noisy optimization problems" (2017). Faculty Publications. 10057.
https://digitalcommons.njit.edu/fac_pubs/10057
