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Global optimization by small-world optimization algorithm based on social relationship network

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Abstract

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jin-hang Li  (李晋航), Xin-yu Shao  (邵新宇), Yuan-ming Long  (龙渊铭) & Hai-ping Zhu  (朱海平)

  2. Air Force Research Laboratory, Wright Patterson Air Force Base, Dayton, 45433, USA

    B. R. Schlessman

Authors
  1. Jin-hang Li  (李晋航)
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  2. Xin-yu Shao  (邵新宇)
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  3. Yuan-ming Long  (龙渊铭)
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  4. Hai-ping Zhu  (朱海平)
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  5. B. R. Schlessman
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Correspondence to Jin-hang Li  (李晋航).

Additional information

Foundation item: Projects(51105157, 50875101) supported by the National Natural Science Foundation of China; Project(2009AA043301) supported by the National High Technology Research and Development Program of China

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Li, Jh., Shao, Xy., Long, Ym. et al. Global optimization by small-world optimization algorithm based on social relationship network. J. Cent. South Univ. 19, 2247–2265 (2012). https://doi.org/10.1007/s11771-012-1269-x

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  • DOI: https://doi.org/10.1007/s11771-012-1269-x

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