To satisfy the distinct requirements of different evolutionary stages, a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) is proposed in this paper. In DMS-GPSO, the entire evolutionary process is segmented as an initial stage and a later stage. In the initial stage, the entire population is divided into a global sub-swarm and multiple dynamic multiple sub-swarms. During the evolutionary process, the global sub-swarm focuses on the exploitation under the guidance of the optimal particle in the entire population, while the dynamic multiple sub-swarms pour more attention on the exploration under the guidance of the neighbor’s best-so-far position. Moreover, a store operator and a reset operator applied in the global sub-swarm are used to save computational resource and increase the population diversity, respectively. At the later stage, some elite particles stored in an archive are combined with the DMS sub-swarms as a single population to search for optimal solutions, intending to enhance the exploitation ability. The effect of the new introduced strategies is verified by extensive experiments. Besides, the comparison results among DMS-GPSO and other 9 peer algorithms on CEC2013 and CEC2017 test suites demonstrate that DMS-GPSO can effectively avoid the premature convergence when solving multimodal problems, and yield more favorable performance in complex problems.

中文翻译：

---

动态多群全局粒子群优化

为了满足不同进化阶段的不同需求，本文提出了一种动态多群全局粒子群优化（DMS-GPSO）。在 DMS-GPSO 中，整个演化过程分为初始阶段和后期阶段。在初始阶段，整个种群被划分为一个全局子群和多个动态多子群。在进化过程中，全局子群侧重于在整个种群中最优粒子引导下的开发，而动态多子群则更注重在邻居最佳粒子引导下的探索。位置。此外，在全局子群中应用的存储算子和重置算子分别用于节省计算资源和增加种群多样性。在后期，一些存储在档案中的精英粒子与 DMS 子群结合为一个群体，寻找最优解，旨在提高开发能力。大量实验验证了新引入策略的效果。此外，CEC2013和CEC2017测试套件上DMS-GPSO与其他9种同行算法的对比结果表明，DMS-GPSO在解决多模态问题时可以有效避免早熟收敛，在复杂问题中表现更佳。