Open asynchronous dynamic cellular learning automata and its application to allocation hub location problem

Cellular learning automata (CLAs) are learning models that bring together the computational power of cellular automata and also the learning capability of learning automata in unknown environments. CLAs can be open or closed. In a closed CLA, the action of each learning automaton depends on the neighboring cells, whereas in an open CLA, the action of each learning automaton depends on the neighboring cells, and a global environment. These models can be synchronous or asynchronous. In a synchronous CLA, all cells are activated at the same time, but in an asynchronous CLA, at a given time only some cells are activated. These models can be also static or dynamic. In a dynamic CLA, one of its aspects such as structure, local rule or neighborhood may vary with time. All existing dynamic models of the CLAs are closed. In this paper, an open asynchronous dynamic CLA has been introduced. In order to show the potential of this model, an algorithm based on this model for solving allocation hub location problem with imprecise distances among nodes has been designed. To evaluate the proposed algorithm computer simulations have been conducted. The results of simulations show that the proposed algorithm is more robust to imprecise distances as compared to existing algorithms.