Applied Intelligence

, Volume 49, Issue 2, pp 335–351 | Cite as

Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems

  • Yong Ning
  • Zishun PengEmail author
  • Yuxing Dai
  • Daqiang Bi
  • Jun Wang


Traditional particle swarm optimization (PSO) algorithm mainly relies on the history optimal information to guide its optimization. However, when the traditional PSO algorithm searches high-dimensional complex problems, wrong position information of the best particles can easily cause the most of the particles move toward wrong space, so the traditional PSO algorithm is easily trapped into local optimum. To improve the optimization performance of the traditional PSO algorithm, an enhanced particle swarm optimization with multi-swarm and multi-velocity (MMPSO) is proposed. It comprises three particle swarms and three velocity update methods. The information sharing of the multi-swarm with various velocity update methods in the MMPSO can quickly discover more useful global information and local information, helping prevent particles from falling into local optimum and improving optimization precision of the algorithm. The MMPSO is tested on fourteen benchmark functions, and is compared with the other improved PSO algorithms. Comparison results validate the validity and feasibility of the MMPSO to optimize high-dimensional problems.


Particle swarm optimization High-dimensional complex problems Information sharing Multi-swarm Multi-velocity 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.College of Physics and Electronic Information EngineeringWenzhou UniversityChangshaChina
  3. 3.State Key Laboratory of Power System, Department of Electrical EngineeringTsinghua UniversityBeijingChina

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