TY - JOUR
T1 - A micro-differential evolution algorithm for continuous complex functions
AU - Olguin-Carbajal, M.
AU - Herrera-Lozada, J. C.
AU - Sandoval-Gutierrez, J.
AU - Vasquez-Gomez, J. I.
AU - Serrano-Talamantes, J. F.
AU - Chavez-Estrada, F. A.
AU - Rivera-Zarate, I.
AU - Hernandez-Bolanos, M.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - In this paper, we incorporate a local search procedure into a micro differential evolution algorithm MED with the aim of solving the HappyCat function. Our purpose is to find out if our proposal is more competitive than a Ray-ES algorithm. We test our micro Differential Evolution algorithm (\mu DE) on HappyCat and HGBat functions. The results that we obtained with micro-DE are better compared with the results the original RayES reference algorithm. This analysis supports our conjecture that a reduced population DE hybridized with a local search (Ray search) is a key combination in dealing with this function. Our results support the hypothesis that a well-focused micro population is more accurate and efficient than existing techniques, representing (that of micro-algorithms) a serious competitor because of its efficiency and accuracy. In fact, the proposed (but never solved) HGBat function can be dealt with, showing the scalability and potential future uses of our technique.
AB - In this paper, we incorporate a local search procedure into a micro differential evolution algorithm MED with the aim of solving the HappyCat function. Our purpose is to find out if our proposal is more competitive than a Ray-ES algorithm. We test our micro Differential Evolution algorithm (\mu DE) on HappyCat and HGBat functions. The results that we obtained with micro-DE are better compared with the results the original RayES reference algorithm. This analysis supports our conjecture that a reduced population DE hybridized with a local search (Ray search) is a key combination in dealing with this function. Our results support the hypothesis that a well-focused micro population is more accurate and efficient than existing techniques, representing (that of micro-algorithms) a serious competitor because of its efficiency and accuracy. In fact, the proposed (but never solved) HGBat function can be dealt with, showing the scalability and potential future uses of our technique.
KW - Happycat
KW - Hgbat
KW - Highly difficult problems
KW - Hybrid algorithms
KW - Micro-algorithms
KW - Rayes
UR - http://www.scopus.com/inward/record.url?scp=85078248397&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2954296
DO - 10.1109/ACCESS.2019.2954296
M3 - Artículo
AN - SCOPUS:85078248397
SN - 2169-3536
VL - 7
SP - 172783
EP - 172795
JO - IEEE Access
JF - IEEE Access
M1 - 8906028
ER -