TY - GEN
T1 - The improved draining method and its application to proper benchmark problems
AU - Okamoto, Takashi
AU - Aiyoshi, Eitaro
PY - 2006/12/1
Y1 - 2006/12/1
N2 - We have proposed "Draining Method (DM)" in [5,6]. DM is based on the Discrete Gradient Chaos Model (DGCM) and the objective function transformation which is developed by the analysis of DGCM. Applying DM to typical benchmark problems, we have confirmed its superior global optimization capability. However, as Liang et al pointed out in [9], typical benchmark problems, such as Rastrigin function, have several considerable problems. Besides, DM has a problem that we need to set Objective Function Value (OFV) of global minima (or desired value) at the start of the search. In this study, we propose to improve draining procedure so that OFV of the global minimum is not needed. Then, we apply the improved DM to more proper benchmark problems which are created by recommended methods in [9]. Through several numerical simulations, we confirm that improved DM is generally effective for proper benchmark problems. This result suggests that improved DM is effective in general situations.
AB - We have proposed "Draining Method (DM)" in [5,6]. DM is based on the Discrete Gradient Chaos Model (DGCM) and the objective function transformation which is developed by the analysis of DGCM. Applying DM to typical benchmark problems, we have confirmed its superior global optimization capability. However, as Liang et al pointed out in [9], typical benchmark problems, such as Rastrigin function, have several considerable problems. Besides, DM has a problem that we need to set Objective Function Value (OFV) of global minima (or desired value) at the start of the search. In this study, we propose to improve draining procedure so that OFV of the global minimum is not needed. Then, we apply the improved DM to more proper benchmark problems which are created by recommended methods in [9]. Through several numerical simulations, we confirm that improved DM is generally effective for proper benchmark problems. This result suggests that improved DM is effective in general situations.
KW - Chaos
KW - Global optimization
KW - Gradient system
KW - Objective function transformation
UR - http://www.scopus.com/inward/record.url?scp=34250773349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34250773349&partnerID=8YFLogxK
U2 - 10.1109/SICE.2006.315724
DO - 10.1109/SICE.2006.315724
M3 - Conference contribution
AN - SCOPUS:34250773349
SN - 8995003855
SN - 9788995003855
T3 - 2006 SICE-ICASE International Joint Conference
SP - 2190
EP - 2195
BT - 2006 SICE-ICASE International Joint Conference
T2 - 2006 SICE-ICASE International Joint Conference
Y2 - 18 October 2006 through 21 October 2006
ER -