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In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

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A Quick Adaptive Galerkin Finite Volume (QAGFV) solution of Cauchy momentum equations for plane elastic problems is presented in this research. A new damping coefficient is introduced to preserve the efficiency of the iterative pseudo-explicit solution procedure. It is shown that the numerical oscillations are not only effectively damped by the proposed damping coefficient, but also that the rate of the convergence of QAGFV algorithm increases. Furthermore, the numerical results show that the proposed coefficient is not sensitive to the spatial discretization. In order to improve the accuracy of the computed stress and displacement fields, an automatic twodimensional h-adaptive mesh refinement procedure is adopted for shape-function-free solution of the governing equations. For verification, two classical problems and their analytical solutions have been investigated. The first is a uniaxial loaded plate with holes, and the second is a cantilever beam under a concentrated load. The results show a good agreement between QAGFV and analytical method. Moreover, the direct and iterative approaches of the finite element method have been implemented in FORTRAN to evaluate the efficiency and accuracy of the presented algorithm. In the end, the corresponding results of some problems have been compared to the QAGFV solutions. The results confirm that the presented h-adaptive QAGFV solver is accurate and highly efficient especially in a large computational domain.