TY - GEN
T1 - Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems
AU - Sallam, Karam M.
AU - Elsayed, Saber M.
AU - Chakrabortty, Ripon K.
AU - Ryan, Michael J.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Recently, many deferential evolution-based algorithms have been developed to solve constrained optimization problems. The performance of these methods outperforms the performance of single operator and/or algorithm-based ones. However, they do not perform consistently for all the problems tested in the literature. Also, the process of using the appropriate selection of algorithms and operators may be time-consuming since their designs are undertaken mainly through trial and error. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple deferential evolution operators, with the best one is emphasized based on the quality and diversity of the population. The performance of the proposed algorithm is tested by solving 57 real-world constrained problems with different dimensions, number of equality and equality constraints, with its results showing a high success rate and that it outperformed different state-of-the-art algorithms.
AB - Recently, many deferential evolution-based algorithms have been developed to solve constrained optimization problems. The performance of these methods outperforms the performance of single operator and/or algorithm-based ones. However, they do not perform consistently for all the problems tested in the literature. Also, the process of using the appropriate selection of algorithms and operators may be time-consuming since their designs are undertaken mainly through trial and error. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple deferential evolution operators, with the best one is emphasized based on the quality and diversity of the population. The performance of the proposed algorithm is tested by solving 57 real-world constrained problems with different dimensions, number of equality and equality constraints, with its results showing a high success rate and that it outperformed different state-of-the-art algorithms.
KW - adaptive operator selection
KW - constrained optimization
KW - differential evolution
KW - evolutionary algorithms
UR - http://www.scopus.com/inward/record.url?scp=85092036451&partnerID=8YFLogxK
U2 - 10.1109/CEC48606.2020.9185722
DO - 10.1109/CEC48606.2020.9185722
M3 - Conference contribution
AN - SCOPUS:85092036451
SN - 9781728169309
T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
SP - 1
EP - 8
BT - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
A2 - Jin, Yaochu
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020
Y2 - 19 July 2020 through 24 July 2020
ER -