A Coevolutionary Memetic Particle Swarm Optimizer

  • Jiarui Zhou
  • Zhen Ji
  • Zexuan Zhu
  • Siping Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


This paper presents a coevolutionary memetic particle swarm optimizer (CMPSO) for the global optimization of numerical functions. CMPSO simplifies the update rules of the global evolution and utilizes five different effective local search strategies for individual improvement. The combination of the local search strategy and its corresponding computational budget is defined as coevolutionary meme (CM). CMPSO co-evolves both CMs and a single particle position recording the historical best solution that is optimized by the CMs in each generation. The experimental results on 7 unimodal and 22 multimodal benchmark functions demonstrate that CMPSO obtains better performance than other representative state-of-the-art PSO variances. Particularly, CMPSO is shown to have higher convergence speed.


Particle swarm optimization coevolution coevolutionary meme local search strategies 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jiarui Zhou
    • 1
    • 2
  • Zhen Ji
    • 1
  • Zexuan Zhu
    • 1
  • Siping Chen
    • 1
    • 2
  1. 1.College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
  2. 2.Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

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