Abstract. The security level of any symmetric key algorithm is directly proportional to its execution time. The algorithm will be more secured if the number of iteration is increase. Soft computing methods are trying to reduce time complexity without compromise the security level. In this paper, an image has been encrypted by number of steps. At first, a key set has been developed based on 16 arbitrary characters and a large number. The image has been diffused in the next step. The key set and diffused image have been computed by genetic algorithm. Here, ring crossover and order changing mutation operator is used in genetic algorithm. The encrypted image is constructed based on logical operation between the diffused image and key. The effectiveness of the algorithm has been tested by number of statistical tests. Finally, a comparative study has been made between our proposed algorithm and some other algorithms. It has been observed that the proposed algorithm has given better result.

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A complex eigenvector is a result of nonproportional damping present in a structural system. However, it is difficult to identify the accurate damping matrix considering the modal sparsity and coordinate sparsity. A nonproportional viscous damping parameter identification is formulated as an unconstrained optimization problem in the present study. The damping coefficient of each element is considered as the design variable for the optimization problem. The objective function is defined using the incomplete complex eigenvectors, which are generated because of the presence of external damping devices in the structure. This objective function is then minimized using standard particle swarm optimization to identify the damping coefficient of the damping matrix. The accuracy and efficiency of the particle swarm optimization are investigated by solving a few numerical problems with simulated measured data. The proposed method works well with the incomplete measured modal data. The current methodology performs satisfactorily with and without noisy data. A comparison study is performed with the existing gradient-based method, and the results show that the proposed method performs better than the existing gradient-based method for the present problem with and without noisy measurement data.