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A Comparison of Four Memetic Particle Swarm Optimization Algorithms for Continuous Optimization

  • Xin Zhang
  • Xingming Liu
  • Mingshuo Liu
  • Shouju Liu
  • Yanyu Xiao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

Particle swarm optimization (PSO) belongs to swarm intelligence category. It is a famous prototype for dealing with continuous optimization problems, and its efficiency can be enhanced by hybrid with local search methods. Based on recently designed four memetic PSO algorithms, this paper investigates the effectiveness and running time of these algorithms. Experiments are conducted on a set of mathematical test functions. The effectiveness of algorithms are compared based on the quality of solutions found in repeated runs. Their running times are compared based on clock time metric. It is found that PSO hybrid with crossover operator is much more effective than the other memetic PSO algorithms.

Notes

Acknowlegdement

The research was supported in part by the National Science Foundation of China (Project No. 61603275), the Applied Basic Research Program of Tianjin (Project No. 15JCYBJC51500) and the Doctoral Fund Project of Tianjin Normal University (Project No. 043-135202XB1602).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xin Zhang
    • 1
  • Xingming Liu
    • 1
  • Mingshuo Liu
    • 1
  • Shouju Liu
    • 1
  • Yanyu Xiao
    • 1
  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina

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