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Study on probability distributions for evolution in modified extremal optimization

Author

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  • Zeng, Guo-Qiang
  • Lu, Yong-Zai
  • Mao, Wei-Jie
  • Chu, Jian

Abstract

It is widely believed that the power-law is a proper probability distribution being effectively applied for evolution in τ-EO (extremal optimization), a general-purpose stochastic local-search approach inspired by self-organized criticality, and its applications in some NP-hard problems, e.g., graph partitioning, graph coloring, spin glass, etc. In this study, we discover that the exponential distributions or hybrid ones (e.g., power-laws with exponential cutoff) being popularly used in the research of network sciences may replace the original power-laws in a modified τ-EO method called self-organized algorithm (SOA), and provide better performances than other statistical physics oriented methods, such as simulated annealing, τ-EO and SOA etc., from the experimental results on random Euclidean traveling salesman problems (TSP) and non-uniform instances. From the perspective of optimization, our results appear to demonstrate that the power-law is not the only proper probability distribution for evolution in EO-similar methods at least for TSP, the exponential and hybrid distributions may be other choices.

Suggested Citation

  • Zeng, Guo-Qiang & Lu, Yong-Zai & Mao, Wei-Jie & Chu, Jian, 2010. "Study on probability distributions for evolution in modified extremal optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1922-1930.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:9:p:1922-1930
    DOI: 10.1016/j.physa.2009.12.055
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    Cited by:

    1. Chen, Yu-Wang & Zhu, Yao-Jia & Yang, Gen-Ke & Lu, Yong-Zai, 2011. "Improved extremal optimization for the asymmetric traveling salesman problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4459-4465.
    2. Huang, Zhendong & Xiao, Renbin, 2013. "An emergent computation approach to the problem of polygon layout with performance constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5074-5088.

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