Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimization problems, is employed for size optimization of steel space trusses. Design examples revealcompetitive performance of the algorithm compared to the other advanced metaheuristics.

Endurance Time (ET) method is a recently developed response-history based analysis procedure for seismic assessment and structural design in which structures are subjected to a predesigned intensifying excitation function, and their performance is evaluated based on their response at different excitation levels. Generating efficient excitation functions, which is essential for functionality of the method, leads to a complex large-scale optimization problem. In this paper, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which has found many applications in solving continuous optimization problems, is employed to produce the excitation functions. The results reveal the good performance of the algorithm in generating ET excitation functions (ETEF) with reasonable accuracy and time efficiency.

Drilling muds are the most applicable fluids in drilling. Two basic types of drilling fluids are used, water based muds (WBM) and oil based muds (OBM). Water based muds are more applicable than oil based muds. One of the most important applications of this fluid is cooling a bit. Chemical engineers try to change drilling mud‘s rheological property in order to increase heat transfer to the bit. Rheological properties of drilling muds are well described by the Herschel Bulkley model. Adding polyacrylic acid to water changes its rheological property to Herschel Bulkley fluid. Standard equations like Shah and London and Hausen correlations were not able to predict local Nusselt number of non-Newtonian fluids. This study concerns estimating parameters of a local Nusselt number of Herschel Bulkley fluids with CuO nanoparticles in four concentrations of 0.1, 0.3, 0.6 and 0.05% in constant heat flux and laminar region. A nonlinear optimization algorithm (CMA-ES) was used to estimate local Nusselt number. There is good agreement between experimental data and those predicted by proposed correlations with R2 greater than 0.99.

The need for planning the scheduling of the user’s jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including everincreasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years.This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, RLPT, GA and PSO algorithms.