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Magnetic Induction Tomography (MIT) is a promising modality for noninvasive imaging due to its contactless and nonionizing technology. In this imaging method, a primary magnetic field is applied by excitation coils to induce eddy currents in the material to be studied, and a secondary magnetic field is detected from these eddy currents using sensing coils. The image (spatial distribution of electrical conductivity) is then reconstructed using measurement data, the initial estimation of electrical conductivity, and the iterative solution of forward and inverse problems. The inverse problem can be solved using one-step linear, iterative nonlinear, and special methods. In general, the MIT inverse problem can be solved by Gauss-Newton iterative method with acceptable accuracy. In this paper, this algorithm is extended and the zoning technique is employed for the reduction of unknown coefficients. The simulation results obtained by the proposed method are compared with the real conductivity coefficients and the mean relative error rate is reduced to 24.22%. On the other hand, Gauss-Newton iterative method is extended for solving the inverse problem of the MIT, and sensitivity measurement matrices are extracted in different experimental and normalization conditions. [ABSTRACT FROM AUTHOR]

In this paper, to solve the multi-objective problem of distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

The deterministic permutation flow shop scheduling problem with makespan criterion is not solvable in polynomial time. Therefore, researchers have thought about heuristic algorithms. There are many heuristic algorithms for solving it that is a very important combinatorial optimization problem. In this paper, a new algorithm is proposed for solving the mentioned problem. The presented algorithm chooses the weighted path that starts from the up-left corner and reaches the down-right in the matrix of jobs processing times and calculates the biggest sum of the times in the footprints of this path. The row with the biggest sum permutes among all the rows of the matrix for locating the minimum of makespan. This method was run on Taillards standard benchmark and the solutions were compared with the optimum or the best ones as well as 14 famous heuristics. The validity and effectiveness of the algorithm are shown with tables and statistical evaluation. [ABSTRACT FROM AUTHOR]