@inproceedings{9cb05e8002e54e9d9afa621e320853bf,
title = "From resampling to non-resampling: A fireworks algorithm-based framework for solving noisy optimization problems",
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.",
keywords = "Fireworks algorithm, Noisy environment, Non-resampling, Resampling",
author = "Zhang, {Jun Qi} and Zhu, {Shan Wen} and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 8th International Conference on Swarm Intelligence, ICSI 2017 ; Conference date: 27-07-2017 Through 01-08-2017",
year = "2017",
doi = "10.1007/978-3-319-61824-1_53",
language = "English (US)",
isbn = "9783319618234",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "485--492",
editor = "Ying Tan and Hideyuki Takagi and Yuhui Shi",
booktitle = "Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings",
address = "Germany",
}