TY - GEN
T1 - Accelerating the fireworks algorithm with an estimated convergence point
AU - Yu, Jun
AU - Takagi, Hideyuki
AU - Tan, Ying
N1 - Funding Information:
Acknowledgment. This work was supported in part by Grant-in-Aid for Scientific Research (JP15K00340) and the Natural Science Foundation of China (NSFC) under grant no. 61673025.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049083589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049083589&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93815-8_26
DO - 10.1007/978-3-319-93815-8_26
M3 - Conference contribution
AN - SCOPUS:85049083589
SN - 9783319938141
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 263
EP - 272
BT - Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Tang, Qirong
PB - Springer Verlag
T2 - 9th International Conference on Swarm Intelligence, ICSI 2018
Y2 - 17 June 2018 through 22 June 2018
ER -