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A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

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  • Haifeng Du
  • Jiarui Fan
  • Xiaochen He
  • Marcus W. Feldman

Abstract

Network structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low average path length. Previous studies have introduced a series of models to generate small-world networks, but few focus on how to improve the efficiency of the generating process. In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world networks by adding edges, and we apply this algorithm to some experimental systems. In the process of using the GSA algorithm, the existence of hubs and disassortative structure is revealed.

Suggested Citation

  • Haifeng Du & Jiarui Fan & Xiaochen He & Marcus W. Feldman, 2018. "A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process," Complexity, Hindawi, vol. 2018, pages 1-12, November.
    • Handle: RePEc:hin:complx:1453898
    DOI: 10.1155/2018/1453898
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    References listed on IDEAS

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    1. Seaton, Katherine A. & Hackett, Lisa M., 2004. "Stations, trains and small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 635-644.
    2. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    3. Neill, Simon P. & Hashemi, M. Reza & Lewis, Matt J., 2014. "Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function," Energy, Elsevier, vol. 73(C), pages 997-1006.
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    1. Ruochen Zhang & Bin Zhu, 2024. "A multiobjective evolutionary algorithm for optimizing the small-world property," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-25, December.

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