In the present research, a type of permutation optimization was introduced. It is assumed that the cost function has an unknown probability distribution function. Since the solution space is inherently large, solving the problem of finding the optimal permutation is com More
In the present research, a type of permutation optimization was introduced. It is assumed that the cost function has an unknown probability distribution function. Since the solution space is inherently large, solving the problem of finding the optimal permutation is complex and this assumption increases the complexity. In the present study, an algorithm based on distributed learning automata was presented to solve the problem by searching in the permutation answer space and sampling random values. In the present research, in addition to the mathematical analysis of the behavior of the proposed new algorithm, it was shown that by choosing the appropriate values of the parameters of the learning algorithm, this new method can find the optimal solution with a probability close to 100% and by targeting the search using the distributed learning algorithms. The result of adopting this policy is to decrease the number of samplings in the new method compared to methods based on standard sampling. In the following, the problem of finding the minimum spanning tree in the stochastic graph was evaluated as a random permutation optimization problem and the proposed solution based on learning automata was used to solve it.
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<p><span style="font-size: 12pt; font-family: 'Times New Roman', serif;">Modularity is one of the prominent features of complex networks that divides the structure of these networks into community groups. So far, many methods have been used to identify communities in co More
<p><span style="font-size: 12pt; font-family: 'Times New Roman', serif;">Modularity is one of the prominent features of complex networks that divides the structure of these networks into community groups. So far, many methods have been used to identify communities in complex networks, but some of these methods have local optimizations that affect the order of processing nodes and the final solution. In this paper, a new method for finding communities in complex networks using split and merge is proposed. In this method, minimum spanning tree is used as a tool to detect dissimilarity between nodes. In the partitioning process, the edges that show the most dissimilarity are removed in the minimum spanning tree to create smaller groups of nodes in a community. In the merging process, each group is merged with the neighboring group whose combination has the highest increase in modularity compared to other neighboring groups. The results of experiments conducted on real networks and artificial networks show that the method proposed in this article has a good accuracy for identifying communities in complex networks. </span></p>
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