A new Reinforcement Learning-based Memetic Particle Swarm Optimizer

• Published in: Applied soft computing Vol. 43; pp. 276 - 297
• Main Authors: Samma, Hussein, Lim, Chee Peng, Mohamad Saleh, Junita
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-06-2016
• Subjects: Local search; Memetic algorithm; Particle Swarm Optimization; Reinforcement learning; Memetic algorithm; Local search; Particle Swarm Optimization; Reinforcement learning
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.01.006

A Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model with four global search operations and one local search operation.
•A Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) is proposed.•Each particle is subject to five possible operations under control of the RL algorithm.•They are exploitation, convergence, high-jump, low-jump, and local fine-tuning.•The operation is executed according to the action generated by the RL algorithm.•The empirical results indicate that RLMPSO outperforms many other PSO-based models.
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.