Scouting strategy for biasing fireworks algorithm search to promising directions

Jun Yu, Ying Tan, Hideyuki Takagi

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

7 被引用数 (Scopus)

抄録

We propose a scouting strategy to find better searching directions in fireworks algorithm (FWA) to enhance its exploitation capability. It generates spark individuals from a firework individual one by one by checking if the generated spark climbs up to a better direction, and this process continues until spark individual climbing down is generated, while canonical FWA generates spark individuals around a firework individual at once. We can know potential search directions from the number of consciously climbing up sparks. Besides this strategy, we use a filtering strategy for a random selection of FWA, where worse sparks are eliminated when their fitness is worse than their parents, i.e. fireworks, and become unable to survive in the next generation. We combined these strategies with the enhanced FWA (EFWA) and evaluated using 28 CEC2013 benchmark functions. Experimental results confirm that the proposed strategies are effective and show better performance in terms of convergence speed and accuracy. Finally, we analyze their applicability and provide some open topics.

本文言語英語
ホスト出版物のタイトルGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ99-100
ページ数2
ISBN(電子版)9781450357647
DOI
出版ステータス出版済み - 7 6 2018
イベント2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, 日本
継続期間: 7 15 20187 19 2018

出版物シリーズ

名前GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

その他

その他2018 Genetic and Evolutionary Computation Conference, GECCO 2018
国/地域日本
CityKyoto
Period7/15/187/19/18

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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