Accelerating the fireworks algorithm with an estimated convergence point

Jun Yu, Hideyuki Takagi, Ying Tan

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

8 被引用数 (Scopus)

抄録

We propose an acceleration method for the fireworks algorithms which uses a convergence point for the population estimated from moving vectors between parent individuals and their sparks. To improve the accuracy of the estimated convergence point, we propose a new type of firework, the synthetic firework, to obtain the correct of the local/global optimum in its local area’s fitness landscape. The synthetic firework is calculated by the weighting moving vectors between a firework and each of its sparks. Then, they are used to estimate a convergence point which may replace the worst firework individual in the next generation. We design a controlled experiment for evaluating the proposed strategy and apply it to 20 CEC2013 benchmark functions of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs each. The experimental results and the Wilcoxon signed-rank test confirm that the proposed method can significantly improve the performance of the canonical firework algorithm.

本文言語英語
ホスト出版物のタイトルAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
編集者Ying Tan, Yuhui Shi, Qirong Tang
出版社Springer Verlag
ページ263-272
ページ数10
ISBN(印刷版)9783319938141
DOI
出版ステータス出版済み - 2018
イベント9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, 中国
継続期間: 6 17 20186 22 2018

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)
10941 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他9th International Conference on Swarm Intelligence, ICSI 2018
国/地域中国
CityShanghai
Period6/17/186/22/18

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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