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

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

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
EditorsYing Tan, Hideyuki Takagi, Yuhui Shi
PublisherSpringer Verlag
Pages477-484
Number of pages8
ISBN (Print)9783319618234
DOIs
Publication statusPublished - Jan 1 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

Fingerprint

Decrease
Strategy
Fitness
Experiments
Benchmark
Evaluate
Experimental Results
Demonstrate
Experiment
Design

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yu, J., & Takagi, H. (2017). Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. In Y. Tan, H. Takagi, & Y. Shi (Eds.), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (pp. 477-484). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61824-1_52

Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. / Yu, Jun; Takagi, Hideyuki.

Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. ed. / Ying Tan; Hideyuki Takagi; Yuhui Shi. Springer Verlag, 2017. p. 477-484 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS).

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

Yu, J & Takagi, H 2017, Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. in Y Tan, H Takagi & Y Shi (eds), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10385 LNCS, Springer Verlag, pp. 477-484, 8th International Conference on Swarm Intelligence, ICSI 2017, Fukuoka, Japan, 7/27/17. https://doi.org/10.1007/978-3-319-61824-1_52
Yu J, Takagi H. Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. In Tan Y, Takagi H, Shi Y, editors, Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Springer Verlag. 2017. p. 477-484. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-61824-1_52
Yu, Jun ; Takagi, Hideyuki. / Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. editor / Ying Tan ; Hideyuki Takagi ; Yuhui Shi. Springer Verlag, 2017. pp. 477-484 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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