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

作     者：李晋航 邵新宇 龙渊铭 朱海平 B.R.Schlessman 

作者机构：School of Mechanical Science and EngineeringHuazhong University of Science and Technology Air Force Research LaboratoryWright Patterson Air Force Base 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2012年第19卷第8期

页      面：2247-2265页

核心收录：

学科分类：07[理学] 0806[工学-冶金工程] 08[工学] 070104[理学-应用数学] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：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 

主　　题：全局优化问题 小世界网络 优化算法 关系网络 社会学 快速收敛算法 网络模型 搜索策略 

摘      要：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.