Study on probability distributions for evolution in modified extremal optimization

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

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 9, 1922-1930

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.

Keywords: Extremal optimization (EO); Probability distributions; Evolution; Self-organized criticality (SOC); Self-organized algorithm (SOA); Traveling salesman problems (TSP) (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:9:p:1922-1930

DOI: 10.1016/j.physa.2009.12.055

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