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 language | English |
|---|---|
| Title of host publication | Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings |
| Editors | Ying Tan, Hideyuki Takagi, Yuhui Shi |
| Publisher | Springer Verlag |
| Pages | 477-484 |
| Number of pages | 8 |
| ISBN (Print) | 9783319618234 |
| DOIs | |
| Publication status | Published - Jan 1 2017 |
| Event | 8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan Duration: Jul 27 2017 → Aug 1 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10385 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 8th International Conference on Swarm Intelligence, ICSI 2017 |
|---|---|
| Country | Japan |
| City | Fukuoka |
| Period | 7/27/17 → 8/1/17 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy
AU - Yu, Jun
AU - Takagi, Hideyuki
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85026738098&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026738098&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61824-1_52
DO - 10.1007/978-3-319-61824-1_52
M3 - Conference contribution
AN - SCOPUS:85026738098
SN - 9783319618234
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 477
EP - 484
BT - Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
A2 - Tan, Ying
A2 - Takagi, Hideyuki
A2 - Shi, Yuhui
PB - Springer Verlag
ER -