TY - GEN
T1 - Engineering benchmark generation and performance measurement of evolutionary algorithms
AU - Kim, Joong Hoon
AU - Lee, Ho Min
AU - Jung, Donghwi
AU - Sadollah, Ali
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/5
Y1 - 2017/7/5
N2 - Various evolutionary algorithms are being developed to search the optimal solution of various problems in the real world. Evolutionary algorithms search solutions showing the optimal fitness to given problem using their own operators. Engineering benchmark problems can be used for performance measurement of evolutionary algorithms, and the water distribution network design problem is one of the widely used benchmark problems. In this study, the water distribution network design problems are generated by modifications of five problem characteristic factors. Generated benchmark problems are applied to quantitatively evaluate the performance among evolutionary algorithms. Each algorithm shows its own strength and weakness. Optimization results show that the engineering benchmark generation method suggested in this study can be served as a reliable framework for comparison of performances on various water distribution network design problems.
AB - Various evolutionary algorithms are being developed to search the optimal solution of various problems in the real world. Evolutionary algorithms search solutions showing the optimal fitness to given problem using their own operators. Engineering benchmark problems can be used for performance measurement of evolutionary algorithms, and the water distribution network design problem is one of the widely used benchmark problems. In this study, the water distribution network design problems are generated by modifications of five problem characteristic factors. Generated benchmark problems are applied to quantitatively evaluate the performance among evolutionary algorithms. Each algorithm shows its own strength and weakness. Optimization results show that the engineering benchmark generation method suggested in this study can be served as a reliable framework for comparison of performances on various water distribution network design problems.
KW - Engineering approach
KW - Evolutionary algorithms
KW - Performance comparison
KW - Water distribution networks
UR - http://www.scopus.com/inward/record.url?scp=85027895913&partnerID=8YFLogxK
U2 - 10.1109/CEC.2017.7969380
DO - 10.1109/CEC.2017.7969380
M3 - Conference contribution
AN - SCOPUS:85027895913
T3 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
SP - 714
EP - 717
BT - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017
Y2 - 5 June 2017 through 8 June 2017
ER -