Search Guidance Network Assisted Dynamic Particle Swarm Optimization Algorithm

Abstract

In dynamic optimization problems (DOPs), environmental changes can be characterized as different dynamics, and adaption of dynamic optimization algorithms (DOAs) in different dynamic environments is vital. In addition, the local and global diversity loss is one of the main reasons behind the degradation of the exploitation and exploration capabilities of DOAs. Maintaining local and global diversity in dynamic environments can effectively avoid diversity loss. To this end, a search guidance network-based particle swarm optimization (SGN-PSO) is proposed. The learning target of each input particle is selected based on the hidden layer of SGN, and its acceleration coefficient is adjusted in the output layer to guide the search of particles. Specifically, SGN is a single-hidden layer radial basis function neural network, and each of its hidden layer nodes consists of a center and radius. By setting multiple hidden nodes whose centers, i.e. the subpopulation centers, are far from each other, multiple subpopulations can be obtained. Each particle selects the local learning target from the personal best historical positions that belong to its subpopulation, and selects the global learning target from the subpopulation centers that are far from each other, contributing to maintaining local and global diversity of the population. Reinforcement learning is employed to obtain the desired output of the input particles and extreme learning machine is utilized to pre-train the network. Furthermore, the significance and crowding degree metrics of hidden nodes are designed to obtain a compact network structure, and incremental learning is used to ensure the network approximation ability. No matter which dynamic occurs, SGN-PSO can adapt to different environments through learning for guiding the search of particles, and can effectively address DOPs of different dynamics. Compared with five mainstream DOAs on MPB and DRPBG benchmark test suites, the results demonstrate that SGN-PSO achieves significant performance improvement in solving DOPs.

Keywords

• dynamic optimization; incremental extreme learning machine; feedforward neural network; particle swarm optimization