IMODEII: an Improved IMODE algorithm based on the Reinforcement Learning

Karam M. Sallam, Mohamed Abdel-Basset, Mohammed El-Abd, Ali Wagdy

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationConference proceedings of the 2022 IEEE Congress on Evolutionary Computation, CEC 2022
EditorsMarco Gori, Alessandro Sperduti , Piero Bonissone
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781665467087
ISBN (Print)9781665467094
Publication statusPublished - 2022
Event2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

Name2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings


Conference2022 IEEE Congress on Evolutionary Computation, CEC 2022

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