A new reinforcement learning-based memetic particle swarm optimizer

Samma, Hussein, Lim, Chee Peng and Saleh, Junita Mohamad 2016, A new reinforcement learning-based memetic particle swarm optimizer, Applied soft computing journal, vol. 43, pp. 276-297, doi: 10.1016/j.asoc.2016.01.006.

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Title A new reinforcement learning-based memetic particle swarm optimizer
Author(s) Samma, Hussein
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Saleh, Junita Mohamad
Journal name Applied soft computing journal
Volume number 43
Start page 276
End page 297
Total pages 22
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-06
ISSN 1568-4946
Summary Developing an effective memetic algorithm that integrates the Particle Swarm Optimization (PSO) algorithm and a local search method is a difficult task. The challenging issues include when the local search method should be called, the frequency of calling the local search method, as well as which particle should undergo the local search operations. Motivated by this challenge, we introduce a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model. Each particle is subject to five operations under the control of the Reinforcement Learning (RL) algorithm, i.e. exploration, convergence, high-jump, low-jump, and fine-tuning. These operations are executed by the particle according to the action generated by the RL algorithm. The proposed RLMPSO model is evaluated using four uni-modal and multi-modal benchmark problems, six composite benchmark problems, five shifted and rotated benchmark problems, as well as two benchmark application problems. The experimental results show that RLMPSO is useful, and it outperforms a number of state-of-the-art PSO-based algorithms.
Language eng
DOI 10.1016/j.asoc.2016.01.006
Field of Research 099999 Engineering not elsewhere classified
0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083090

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