A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning

作者：Su Mu Chun^*; Zhao Yu Xiang

来源：Neural Computing & Applications, 2009, 18(8): 1043-1055.

DOI：10.1007/s00521-009-0278-7

• Optimization
• Self-organizing feature map (SOM)
• Evolutionary computing
• Genetic algorithm (GA)

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.

• 出版日期2009-11
• 单位中央民族大学