关键词: 小世界现象, 精英集聚效应, 长连接, 短连接, 实数编码

Abstract: To improve the high dimensional model optimization performance of small world algorithm and reduce the complexity of algorithm coding, this paper proposes an Adaptive Real Coding Small World Algorithm based on elite gathering effect（ARCSWA）. Inspired by small world phenomena, the algorithm performs network search, including random long-range connection and local short-range connection. To improve the optimization performance, the hierarchical individual attraction strategy is firstly added to the long-range connection based on the elite gathering effect. Then, the number of searches and the size of the domain are adaptively adjusted in the short-range connection according to the node optimization. In addition, real coding is used to reduce coding complexity. Finally, the convergence of the ARCSWA is proved by Markov chain theory. The numerical test results show that, compared with the Tabu Genetic Algorithm（TGA）, the Simple Small World Algorithm（SSWA） and the Tabu Small World Algorithm（TSWA）, the average error of the proposed algorithm is reduced by 30.3% the convergence speed and stability of the proposed algorithm are increased by 18.2% and 13.8%, respectively, which verifies the validity of the ARCSWA.

Key words: small world phenomena, elite gathering effect, long-range connection, short-range connection, real coding