基于深度确定性策略梯度的粒子群算法

A Particle Swarm Optimization Algorithm Based on Deep Deterministic Policy Gradient

在传统的粒子群优化算法(PSO)中,所有粒子都遵循最初设定的一些参数进行自我探索,这种方案容易导致过早成熟,且易被困于局部最优点。针对以上问题,该文提出了一种基于深度确定性策略梯度的粒子群优化算法(DDPGPSO),通过构造神经网络分别实现了动作函数和动作价值函数,且利用神经网络可以动态地生成算法运行所需要的参数,降低了人工配置算法的难度。实验表明DDPGPSO相比9种同类算法在收敛速度和寻优精度上均有较大的提升。

In the traditional particle swarm optimization(PSO)algorithm,all particles follow some initial parameters to explore themselves.This scheme is easy to lead to premature maturity,and easy to be trapped in the local optimum.To solve the above problems,a particle swarm optimization algorithm based on deep deterministic policy gradient(DDPGPSO)is proposed.The action function and action value function are realized by constructing neural network.The parameters required by the algorithm can be generated dynamically by using the neural network,which reduces the difficulty of manual configuration of the algorithm.The experimental results show that DDPGPSO has a great improvement in convergence speed and optimization accuracy compared with nine similar algorithms.