We investigate how delayed communication a ects the dynamic formation of groups in distributed systems, where all decision-making agents join the same group because each expects to improve its own performance. For example, distributed job schedulers may form a group to utilize the idle resources of other members within the group. Forming a group is a search problem and we examine agents which u...

We investigate how delayed communication affects the dynamic formation of groups in distributed systems, where all decision-making agents join the same group because each expects to improve its own performance. For example, distributed job schedulers may form a group to utilize the idle resources of other members within the group. Forming a group is a search problem and we examine agents that u...

In this paper an algorithm based on distributed learning automata for finding minimum spanning tree in a stochastic graph when the weight density functions for the edges of the graph is unknown is propped. The proposed algorithm with the aid of distributed learning automata tries to find the minimum spanning tree with minimum number of sampling from the edges of the stochastic graph. Using simu...

A key problem in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic distributed agent systems. As the state space grows, agent policies become more and more complex and learning slows. One possible solution for an agent to continue learning in these large-scale systems is to learn a policy which generalizes over states, rather than...

Please cite this article in press as: J. Akbari Tork learning automata approach, Comput. Commun During the last decades, many studies have been conducted on multicast routing in mobile ad hoc networks (MANET) and a host of algorithms have been proposed. In existing algorithms, the mobility characteristics are assumed to be constant, and so they do not scale well when the mobility parameters are...

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.01.098 ⇑ Corresponding author. E-mail addresses: [email protected] (J. Akbari ac.ir (M.R. Meybodi). Vertex coloring problem is a combinatorial optimization problem in which a color is assigned to each vertex of the graph such that no two adjacent vertices have the same color. Cellular learning automata (CLA) is an e...

In adaptive resonance theory network, the choice of vigilance factor (VF) and choice parameter. (CP) affects the performance of the network, such as the number of classes into which the data are classified. These parameters are typically chosen and adapted using human judgment, experience, and heuristic rules. Rather than choosing and optimizing these parameters manually, we use learning automa...

Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This rese...

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, beca...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...