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.