SOM-based Optimization

Mu Chun Su, Yu Xiang Zhao, Jonathan Lee

Research output: Contribution to journalConference articlepeer-review

33 Scopus citations


In this paper, a new approach to optimization problems based on the self-organizing feature maps is proposed. We name the new optimization algorithm the SOM-based optimization (SOMO) algorithm. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited. An additional advantage of the algorithm is that the outputs of the neural network allow us to transform a multi-dimensional fitness landscape into a three-dimensional projected fitness landscape. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.

Original languageEnglish
Pages (from-to)781-786
Number of pages6
JournalIEEE International Conference on Neural Networks - Conference Proceedings
StatePublished - 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004


  • Evolutionary computing
  • Genetic algorithm
  • Optimization
  • Self-organizing feature map


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