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《中南大学学报(英文版)》 2012-08
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Global optimization by small-world optimization algorithm based on social relationship network

LI Jin-hang1, SHAO Xin-yu1, LONG Yuan-ming1, ZHU Hai-ping1, B. R. Schlessman2 1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; 2. Air Force Research Laboratory, Wright Patterson Air Force Base, Dayton 45433, U.S.A  
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.
【Fund】: 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
【CateGory Index】: TP301.6;O157.5
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