TY  - EJOU
AU  - Yang-Yang, Sun 
AU  - Jun-Ping, Yao 
AU  - Xiao-Jun, Li 
AU  - Shou-Xiang, Fan 
AU  - Zi-Wei, Wang 

TI  - Optimizing the Multi-Objective Discrete Particle Swarm Optimization Algorithm by Deep Deterministic Policy Gradient Algorithm
T2  - Journal on Artificial Intelligence

PY  - 2022
VL  - 4
IS  - 1
SN  - 2579-003X

AB  - 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.
KW  - Deep deterministic policy gradient; multi-objective discrete particle swarm optimization; deep reinforcement learning; machine learning

DO  - 10.32604/jai.2022.027839