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Optimizing the Multi-Objective Discrete Particle Swarm Optimization Algorithm by Deep Deterministic Policy Gradient Algorithm
Xi an High-Tech Institute, Xi an, 710025, China
* Corresponding Author: Yao Jun-Ping. Email:
Journal on Artificial Intelligence 2022, 4(1), 27-35. https://doi.org/10.32604/jai.2022.027839
Received 26 January 2022; Accepted 09 March 2022; Issue published 16 May 2022
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Deep deterministic policy gradient (DDPG) has been proved to be effective in optimizing particle swarm optimization (PSO), but whether DDPG can optimize multi-objective discrete particle swarm optimization (MODPSO) remains to be determined. The present work aims to probe into this topic. Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO, but also overcome the problem of local optimal solution that MODPSO may suffer. The research findings are of great significance for the theoretical research and application of MODPSO.Keywords
Deep deterministic policy gradient; multi-objective discrete particle swarm optimization; deep reinforcement learning; machine learning
Cite This Article
S. Yang-Yang, Y. Jun-Ping, L. Xiao-Jun, F. Shou-Xiang and W. Zi-Wei, "Optimizing the multi-objective discrete particle swarm optimization algorithm by deep deterministic policy gradient algorithm," Journal on Artificial Intelligence, vol. 4, no.1, pp. 27–35, 2022. https://doi.org/10.32604/jai.2022.027839
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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