From resampling to non-resampling: A fireworks algorithm-based framework for solving noisy optimization problems

Jun Qi Zhang, Shan Wen Zhu, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
EditorsYing Tan, Hideyuki Takagi, Yuhui Shi
PublisherSpringer Verlag
Pages485-492
Number of pages8
ISBN (Print)9783319618234
DOIs
StatePublished - 2017
Event8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan
Duration: Jul 27 2017Aug 1 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10385 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Swarm Intelligence, ICSI 2017
CountryJapan
CityFukuoka
Period7/27/178/1/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Fireworks algorithm
  • Noisy environment
  • Non-resampling
  • Resampling

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