A simplified and efficient particle swarm optimization algorithm considering particle diversity

  • Ya Bi
  • Mei Xiang
  • Florian Schäfer
  • Alan Lebwohl
  • Cunfa Wang


In this paper, a dynamic self-adapting and simple particle swarm optimization algorithm with the disturbed extremum and crossover is proposed in order to improve the problem of particle swarm optimization in dealing with high-dimensional multi-extremum problem which is easy to fall into the local extremum and the accuracy of search and speed of the rapid decline problem in the late evolution. The dynamic self-adapting inertia weight and simplified speed equation strategy reduce the computational difficulty of the algorithm and improve the problem of slow convergence and low precision of the evolutionary algorithm due to the particle divergence caused by the velocity term; Extreme value perturbation and hybridization strategies are used to adjust the global extremes and individual positions of the particles to ensure the diversity and vigor of the particles in the late evolutionary period, and improve the ability of the particles to get rid of the local extremes. Three sets of computational experiments are carried out to compare and evaluate the search speed, convergence accuracy and population diversity of the improved algorithm, the results show that the improved algorithm has obtained a very good optimization effect and improved the practicability of the particle swarm optimization algorithm. It shows that the improved algorithm has improved the search speed, precision and population diversity of the optimization algorithm which improves the practicability of the particle swarm algorithm and achieves the expected effect.


Diversity of particle Dynamic self-adapting Simple particle swarm optimization algorithm Local extremum 



This work is supported by the National Natural Science Foundation of China (No. 70160376), the Natural Science Foundation of Hubei Province (No. 2016CFB490), the China Postdoctoral Special Science Foundation (No. 2017T100560) and Hubei Logistic Development Research Center Sponsored Project.


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Copyright information

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

Authors and Affiliations

  • Ya Bi
    • 1
    • 2
  • Mei Xiang
    • 2
  • Florian Schäfer
    • 3
  • Alan Lebwohl
    • 4
  • Cunfa Wang
    • 5
    • 6
  1. 1.College of Public Administration of Huazhong University of Science & TechnologyWuhanChina
  2. 2.School of Logistics and Engineering ManagementHubei University of EconomicsWuhanChina
  3. 3.Accadis Hochschule Bad HomburgFrankfurtGermany
  4. 4.Univ ManchesterManchesterUK
  5. 5.School of ManagementWuhan University of TechnologyWuhanChina
  6. 6.Fujian Zhuozhi Project Investment Consulting Co., LTDWuhanChina

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