TY - GEN
T1 - IMODEII
T2 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022
AU - Sallam, Karam M.
AU - Abdel-Basset, Mohamed
AU - El-Abd, Mohammed
AU - Wagdy, Ali
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.
AB - The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.
KW - differential evolution
KW - evolutionary algorithms
KW - reinforcement learning
KW - unconstrained optimisation
UR - http://www.scopus.com/inward/record.url?scp=85138692171&partnerID=8YFLogxK
U2 - 10.1109/CEC55065.2022.9870420
DO - 10.1109/CEC55065.2022.9870420
M3 - Conference contribution
AN - SCOPUS:85138692171
SN - 9781665467094
T3 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
SP - 1
EP - 8
BT - Conference proceedings of the 2022 IEEE Congress on Evolutionary Computation, CEC 2022
A2 - Gori, Marco
A2 - Sperduti , Alessandro
A2 - Bonissone, Piero
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
Y2 - 18 July 2022 through 23 July 2022
ER -