Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization

Authors:
Sitao Wu;T. W.S. Chow
Affiliations:
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon;-
Venue:
IEEE Transactions on Neural Networks
Year:
2007

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Abstract

A self-organizing and self-evolving agents (SOSENs) neural network is proposed. Each neuron of the SOSENs evolves itself with a simulated annealing (SA) algorithm. The self-evolving behavior of each neuron is a local improvement that results in speeding up the convergence. The chance of reaching the global optimum is increased because multiple SAs are run in a searching space. Optimum results obtained by the SOSENs are better in average than those obtained by a single SA. Experimental results show that the SOSENs have less temperature changes than the SA to reach the global minimum. Every neuron exhibits a self-organizing behavior, which is similar to those of the self-organizing map (SOM), particle swarm optimization (PSO), and self-organizing migrating algorithm (SOMA). At last, the computational time of parallel SOSENs can be less than the SA