In this paper, we introduce open CELLULAR LEARNING AUTOMATA and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open CELLULAR LEARNING AUTOMATA in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory.  

A CELLULAR LEARNING automaton (CLA) is a collection of LEARNING AUTOMATA arranged in a ‎grid similar to CELLULAR AUTOMATA and interacts with each other. Each LEARNING automaton ‎based on the actions chosen by their neighbors tries to find its best action in order for the ‎CELLULAR LEARNING AUTOMATA to reach a particular goal. In this paper several applications of ‎CELLULAR LEARNING AUTOMATA to designing image processing operations such as noise ‎removal image segmentation and feature extraction are presented. The proposed ‎algorithms have number of good characteristics such as: effectiveness in the presence of ‎noise higher accuracy .comparing to other image processing algorithms parallel nature ‎and locality.‎

In this paper, a new algorithm which is the result of combination of CELLULAR LEARNING AUTOMATA (CLA) and shuffled frog leap algorithm (SFLA) is proposed for optimization of functions in continuous, static environments. In the frog leaping algorithm, every frog represents a feasible solution within the problem space. In the proposed algorithm, each memeplex of frogs is placed in a cell of CLA. LEARNING AUTOMATA in each cell acts as the brain of memeplex and will determine the strategy of motion and search. The proposed algorithm along with the standard SFLA and two global and local versions of particle swarm optimization algorithm have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed algorithm has a performance of the introduced algorithm is due to the control of search behavior of frogs during the optimization process.

In this paper, a new algorithm based on three steps has been proposed for segmentation of SAR images. In the first segmentation step, the input SAR satellite image is proposed using CLA and the difference between the gray levels of pixels in each region of SAR image is reduced and a basic segmented image is produced which is used as an input image to the second step of the algorithm. In the second step which is an expletive segmentation step, a q threshold is selected as a gray level distance between the pixels of each region. If the gray level distance between two adjacent pixels is smaller than q, then these two pixels are related to same region and will take the same label. After labeling the regions, the average of the same labeled pixels of gray level is considered as gray level indicator of each region. Theresholding is the last stage of the proposed algorithm in segmentation process for identification of regions. In this step, all of the same regions, take the similar label. The proposed algorithm is evaluated on two types of SAR images: the simulated SAR images and the real SAR images. The results show that the proposed method has less error rate and higher accuracy in comparison with other methods for segmentation of SAR images.

Computing infrastructures that are based on grid networks have been recognized as a basis for new infrastructures of distributed computing. Providing appropriate mechanisms for scheduling and allocating resources to user’s requests in these networks is considered very important. One of the current issues in the grid networks is how to ensure the precise timing of executing requests sent by users, especially requests that have deadlines and also co-allocation requests. The resource reservation has been mainly developed to address this problem in the grid systems. On the other hand, models based on the CELLULAR AUTOMATA have advantages such as lower processing complexity, configurability of the cells, and the ability of predicting future conditions. In this study, an efficient model based on irregular CELLULAR LEARNING AUTOMATA (ICLA) is presented for the task of resource reservation. The proposed model was simulated on a network with random topology structure. The performance of proposed method was compared with two well-known algorithms in this field. The experimental results showed increased efficiency in the resource utilization, decreased process execution delays, and reduced rate of request rejection.

Job shop scheduling problem (JSSP), as one of the NP-Hard combinatorial optimization problems, has attracted the attention of many researchers during the last four decades. The overall purpose regarding this problem is to minimize maximum completion time of jobs, known as makespan. This paper addresses an approach to evolving CELLULAR LEARNING AUTOMATA (CLA) in order to enable it to solve the JSSP by minimizing the makespan. This approach is applied to several instances of a variety of benchmarks and the experimental results show that it produces nearly optimal solutions, compared with other approaches.

In this paper an application of CELLULAR LEARNING AUTOMATA (CLA) to VLSI placement is presented. The CLA, which is introduced for the first time in this paper, is different from standard CELLULAR LEARNING AUTOMATA in two respects. It has input and the cell neighborhood varies during the operation of CLA. The proposed CLA based algorithm for VLSI placement is tested on number of placement problems and has been compared with several reported algorithms such as: simulated annealing, genetic algorithms, the algorithm proposed by Saheb Zamani and Hellestrand, and the algorithm based on Kohenen neural network. The results obtained show that the proposed algorithm produces results, which are comparable to the other algorithms reported in the literatures. The parallel nature of CLA makes it appropriate for hardware implementation.