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.

Key words: dynamic optimization, incremental extreme learning machine, feedforward neural network, particle swarm optimization

摘要： 在动态优化问题（DOP）中环境的变化可描述为不同类型的动态，动态优化算法（DOA）对环境的适应性十分重要。此外，DOA的局部和全局多样性损失是导致其开发和勘探能力下降的主因之一。在动态环境中保持局部和全局多样性可有效避免多样性损失。为此，提出一种基于搜索引导网络的粒子群优化算法（SGN-PSO），每个输入粒子基于SGN隐藏层选择学习目标，在输出层调整其加速系数，从而引导粒子的搜索。SGN属于单隐层径向基神经网络，每个隐藏节点由其中心和半径组成。设置多个相互远离的隐藏节点中心，即子群中心，从而获得多个子群。每个粒子从其所属子群不同个体历史最优位置中选择局部学习目标，从相互远离的多个子群中心中选取全局学习目标，有助于种群的局部和全局多样性保持。SGN以强化学习方式来获得输入粒子的期望输出，并通过极限学习来预训练网络。设计节点的重要性和拥挤度指标，以获取紧凑网络结构，并增量学习保证网络拟合能力。无论环境如何变化，所提方法都能够通过学习来适应不同的环境，以引导粒子的搜索，从而有效处理不同动态的DOP。在MPB和DRPBG标准测试组件上和五种主流DOA开展对比实验，结果表明，SGN-PSO在求解多种动态的DOP上取得了显著的表现提升。

关键词: 动态优化, 增量极限学习机, 前馈神经网络, 粒子群优化