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

Authors:
Mu-Chun Su;Yu-Xiang Zhao
Affiliations:
National Central University, Department of Computer Science and Information Engineering, Chungli, Taiwan;Ta Hwa Institute of Technology, Department of Computer Science and Information Engineering, Hsinchu, Taiwan
Venue:
Neural Computing and Applications
Year:
2009

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MaxMin-SOMO: an SOM optimization algorithm for simultaneously finding maximum and minimum of a function

ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I

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Abstract

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.