Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

10 被引用数 (Scopus)

抄録

We propose two strategies for improving the performance of the Fireworks Algorithm (FWA). The first strategy is to decrease the amplitude of each firework according to the generation, where each firework has the same initial amplitude and decreases in size every generation rather than by dynamic allocation based on its fitness. The second strategy is a local optima-based selection of a firework in the next generation rather than the distance-based selection of the original FWA. We design a set of controlled experiments to evaluate these proposed strategies and run them with 20 benchmark functions in three different dimensions of 2-D, 10-D and 30-D. The experimental results demonstrate that both of the two proposed strategies can significantly improve the performance of the original FWA. The performance of the combination of the two proposed strategies can further improve that of each strategy in almost all cases.

本文言語英語
ホスト出版物のタイトルAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
編集者Ying Tan, Hideyuki Takagi, Yuhui Shi
出版社Springer Verlag
ページ477-484
ページ数8
ISBN(印刷版)9783319618234
DOI
出版ステータス出版済み - 2017
イベント8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, 日本
継続期間: 7 27 20178 1 2017

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10385 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他8th International Conference on Swarm Intelligence, ICSI 2017
国/地域日本
CityFukuoka
Period7/27/178/1/17

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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