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• W2967888962 abstract "This paper proposes a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) that consists of two novel strategies to balance the exploration and exploitation abilities. In DMS-GPSO, the entire population is divided into a global sub-swarm and dynamic multiple sub-swarms. During the evolutionary process. the global sub-swarm focus on exploitation under the guidance of the optimal particle in the entire population, while the dynamic multiple sub-swarms focus on exploration under the guidance of the neighbors best-so-far position. Moreover, the store-reset strategy of the global sub-swarm is applied to save computational resource and increase population diversity, aiming to improve the exploration ability of DMS-GPSO at the initial evolutionary stage. At the later evolutionary stage, some favorable particles of the global sub-swarm stored in an archive are combined with particles in the DMS sub-swarms as a single population to search for optimal solutions, intending to enhance the exploitation ability. The comparison results between DMS-GPSO and other 7 peer algorithms on the CEC2013 test suite demonstrate that DMS-GPSO can avoid the premature convergence when solving multimodal problems, and yields more effective performance in complex problems." @default.
• W2967888962 created "2019-08-22" @default.
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• W2967888962 date "2019-06-01" @default.
• W2967888962 modified "2023-09-26" @default.
• W2967888962 title "Dynamic Multi-swarm Global Particle Swarm Optimization" @default.
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• W2967888962 doi "https://doi.org/10.1109/cec.2019.8790324" @default.
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