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The networked evolutionary algorithm: A network science perspective

Author

Listed:
  • Du, Wenbo
  • Zhang, Mingyuan
  • Ying, Wen
  • Perc, Matjaž
  • Tang, Ke
  • Cao, Xianbin
  • Wu, Dapeng

Abstract

The evolutionary algorithm is one of the most popular and effective methods to solve complex non-convex optimization problems in different areas of research. In this paper, we systematically explore the evolutionary algorithm as a networked interaction system, where nodes represent information process units and connections denote information transmission links. Within this networked evolutionary algorithm framework, we analyze the effects of structure and information fusion strategies, and further implement it in three typical evolutionary algorithms, namely in the genetic algorithm, the particle swarm optimization algorithm, and in the differential evolution algorithm. Our results demonstrate that the networked evolutionary algorithm framework can significantly improve the performance of these evolutionary algorithms. Our work bridges two traditionally separate areas, evolutionary algorithms and network science, in the hope that it promotes the development of both.

Suggested Citation

  • Du, Wenbo & Zhang, Mingyuan & Ying, Wen & Perc, Matjaž & Tang, Ke & Cao, Xianbin & Wu, Dapeng, 2018. "The networked evolutionary algorithm: A network science perspective," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 33-43.
    • Handle: RePEc:eee:apmaco:v:338:y:2018:i:c:p:33-43
    DOI: 10.1016/j.amc.2018.06.002
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    References listed on IDEAS

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    1. Chen Liu & Wen-Bo Du & Wen-Xu Wang, 2014. "Particle Swarm Optimization with Scale-Free Interactions," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    2. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
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