This paper introduces a variant version of the AO for efficiently solving high-dimensional computationally expensive problems. Traditional optimization techniques struggle with problems characterized by expensive objective functions and a large number of variables. To address this challenge, this paper proposes a SMAO that leverages machine learning techniques to approximate the objective function. SMAO utilizes RBF to build an accurate and efficient surrogate model. By iteratively optimizing the surrogate model, the search process is directed toward the global optimum while significantly reducing the computational cost compared to traditional optimization methods. To evaluate and compare the performance of SMAO with the surrogate model-based versions of the Gazelle Optimization Algorithm GOA, RSA, PDO, and FLA, they are analyzed on a set of benchmark test functions with dimensions varying from 30 to 200. According to the reported results, SMAO has a higher performance compared to others in terms of achieving the nearest solutions to an optimum, early convergence, and accuracy.

This paper proposes an efficient meta-heuristic method called expert groups' optimization algorithm. The method strategy relies on four principles and starts from a random initial population. The population members are divided into two expert groups: the free group and the guided group. Each group has specific tasks for effective domain search, but with one new operator. This operator has an intelligent mechanism so that exploration and exploitation of the population can lead the members to the global optimum. The new method is validated through a standard test function. Then its performance is evaluated in the application of an inverse geometric reconstruction and the results are compared with a genetic algorithm, particle swarm optimization, and mean-variance mapping optimization. Results show that the new method outperforms the alternative methods in convergence rate and reaching the global optimum. Finally, the expert groups' optimization algorithm performance is evaluated in an engineering problem with high computational cost. In this case, the goal is drag coefficient minimization of the RAE 2822 airfoil in transonic flow at a fixed lift coefficient with constraints on the pitching moment and airfoil area. An unstructured grid Navier-Stokes flow solver with a two-equation turbulence model is used to evaluate the aerodynamic objective function. The results show that the optimal solutions obtained by the new method outperform those of mean-variance mapping optimization with considerably faster convergence.

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC-LRFD specifications. To this end an upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses in metaheuristic optimization algorithms, is used in conjunction with an exponential variant of the well-known big bang-big crunch optimization algorithm. The performance of the UBS integrated algorithm is investigated in the optimum design of two Large-scale steel frame structures with 3860 and 11540 structural members. The obtained numerical results clearly reveal the usefulness of the employed technique in practical optimum design of large-scale structural systems even using regular computers.

The present study attempts to apply an efficient yet simple optimization (SOPT) algorithm to optimum design of truss structures under stress and displacement constraints. The computational efficiency of the technique is improved through avoiding unnecessary analyses during the course of optimization using the so-called upper bound strategy (UBS). The efficiency of the UBS integrated SOPT algorithm is evaluated through benchmark sizing optimization problems of truss structures and the numerical results are reported. A comparison of the numerical results attained using the SOPT algorithm with those of modern metaheuristic techniques demonstrates that the employed algorithm is capable of locating promising designs with considerably less computational effort.

Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line– area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow‑ angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2. 9 times faster. Differences in root mean square error and peak signal‑ to‑ noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0. 05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0. 05), though computations were 4. 9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM– AIM that achieves significantly improved images though with its computational requirements.

A multi-secret sharing scheme (MSS) allows a dealer to share multiple secrets among a set of participants, in such a way A multi-secret sharing scheme (MSS) allows a dealer to share multiple secrets among a set of participants, in such a way that any authorized subset of participants can reconstruct the secrets. Up to now, existing MSSs either require too long shares for participants to be perfect secure, or do not have a formal security analysis/proof. In 2013, Herranz et al. provided the first formal definition of computational security for multi-stage secret sharing scheme (MSSS) in the standard model and proposed a practical and secure scheme. As far as we know, their scheme is the only computational secure MSS in the standard model, and there is no formal definition of the computational security for other categories of MSSs. Based on this motivation, in this paper, we de ne the first formal model of in distinguishability against the chosen secret attacks (CSA) for other types of MSSs in the standard model. Furthermore, we present two practical CSA-secure MSSs, belonging to the different types of MSSs and enjoying the advantage of short shares. They are also provably secure in the standard model. Based on the semantic security of the underlying encryption schemes, we prove the security of our schemes.