• Àüü
  • ÀüÀÚ/Àü±â
  • Åë½Å
  • ÄÄÇ»ÅÍ
´Ý±â

»çÀÌÆ®¸Ê

Loading..

Please wait....

±¹³» ³í¹®Áö

Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ ³í¹®Áö

Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ ³í¹®Áö

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Hybrid Fireworks Algorithm with Dynamic Coefficients and Improved Differential Evolution
¿µ¹®Á¦¸ñ(English Title) Hybrid Fireworks Algorithm with Dynamic Coefficients and Improved Differential Evolution
ÀúÀÚ(Author) Lixian Li   Jaewan Lee  
¿ø¹®¼ö·Ïó(Citation) VOL 22 NO. 02 PP. 0019 ~ 0027 (2021. 04)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(English Abstract)
Fireworks Algorithm (FWA) is a new heuristic swarm intelligent algorithm inspired by the natural phenomenon of the fireworks explosion. Though it is an effective algorithm for solving optimization problems, FWA has a slow convergence rate and less information sharing between individuals. In this paper, we improve the FWA. Firstly, explosion operator and explosion amplitude are analyzed in detail. The coefficient of explosion amplitude and explosion operator change dynamically with iteration to balance the exploitation and exploration. The convergence performance of FWA is improved. Secondly, differential evolution and commensal learning (CDE) significantly increase the information sharing between individuals, and the diversity of fireworks is enhanced. Comprehensive experiment and comparison with CDE, FWA, and VACUFWA for the 13 benchmark functions show that the improved algorithm was highly competitive.
Å°¿öµå(Keyword) Fireworks Algorithm   Dynamic Coefficient   Differential Evolution   Commensal Learning  
ÆÄÀÏ÷ºÎ PDF ´Ù¿î·Îµå