Self-organizing and self-evolving neurons: a new neural network for optimization.
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.
Wu
Sitao
S
Department of Electronic Engineering, City University of Hong Kong, Kowloon SAR, Hong Kong.
Chow
Tommy W S
TW
D000465:Algorithms; D001185:Artificial Intelligence; D005246:Feedback; D016247:Information Storage and Retrieval / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*