Neural Network-Assisted Particle Swarm Dynamic Optimization

In the field of optimization, many problems change over time and they are referred to dynamic optimization prob-lems (DOPs). faced with DOPs, how to adapt to environmental changes and find the optima are challenging. In this paper, we propose a neural network-assisted particle swarm optimization (NN-PSO) algorithm and the search-guided neural networks (SGNN) are designed. Each particle selects its local and global learning targets based on the hidden nodes of SGNN. Besides, in the output layer, SGNN adjusts the local acceleration coefficient of each particle and hence the global one considering the relation between the two coefficients. A reinforcement learning manner is employed to obtain the desired output of SGNN. We define the significance and crowding degree metrics of hidden nodes, which aims to obtain a compact structure of SGNN. Incremental learning is leveraged to ensure the network approximation ability. In our experiments, we compare the proposed NN-PSO with five state-of-the-art dynamic optimization algorithms on moving peaks benchmark (MPB) benchmark test suite. The experimental results demonstrate that our algorithm achieves significant performance improvement in solving DOPs.