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Abstract Functionally graded beams (FGBs) have been studied extensively in literature to predict their static stability and dynamic behavior. In typical FGBs, each of material variation has significant influence on overall beams’ strength. This research utilized single and multi-objective optimization approaches to optimize the volume fraction distribution in the Timoshenko FGBs in order to maximize the fundamental frequency while minimizing mass and axial stresses. The FGBs with different ratios of material properties are considered. Several metaheuristic optimization techniques are utilized to address single and multi-objective problems. For validating purpose, computational optimization results are compared with those available in the literature. [ABSTRACT FROM AUTHOR]

The generation rescheduling is described as the power generation shifting from one or more generators to one or more other generators as a preventive action to improve and maintain the security of the power system. Since, there is a direct link between security improvement and the lines overload under contingencies, by rescheduling generation, the transmission lines become more flexible and thus, the overload can be relieved. In this paper, contingency constrained optimal power flow (CCOPF) problem based on generation rescheduling by considering the uncertainty of photovoltaic (PV), wind turbine (WT), and plug-in hybrid electric vehicle (PHEV) have been addressed. Water cycle algorithm (WCA) using its potential in finding optimal solution has been used in order to reschedule the generators and optimize the total fuel cost, power losses under contingency scenario, and system security. Moreover, stochastic approach has been proposed for taking into account the uncertainty of PV, WT, and PHEV. Overall performance index including the power and the voltage severity indices have been provided for determining overloaded transmission lines due to the lines’ outage and consequently elimination of overloaded lines. The efficiency of the proposed algorithm has been evaluated on two IEEE-30 and IEEE 118-bus systems. The results are compared with the results of other classical and metaheuristic optimization algorithms. The simulations reveal that the WCA outperforms the other reported optimizers, and is more efficient and effective in improving security for power systems. In addition, the obtained numerical results show that renewable energy sources can significantly reduce fuel costs. [ABSTRACT FROM AUTHOR]

In this paper, conductive polymer-based composites in order to have higher electrical conductivity have been constructed using different nanoparticles and numerically considered by different classification techniques. Due to non-conducting feature of polymer-based composites, their other positive advantages (e.g., light weight and stress corrosion) underneath non-conducting defect in which this paper has tried to overcome the faced challenges. For this purpose, carbon black (CB), carbon nanotube (CNT), and expanded graphite (EG) with different weight percentages are added to the epoxy resin as input factors and the electrical conductivity of the samples are measured as response factor. The analysis of input factors is performed and the Taguchi method, artificial neural networks (ANNs) and extreme learning machine (ELM) are designed and used for the prediction of the response factor. The predicted responses using the applied methods are compared with the experimental results. In order to increase the mechanical strength, ten layers of unidirectional carbon fiber are used. The simulation results show that the ANNs and ELM provide good compatible predictions with respect to actual experiment data. Besides, obtained experimental results prove that the highest electrical conductivity has been achieved using 10, 15, and 25 percent using the CNT, EG, and CB, respectively. As a novelty of this paper, the constructed sample composite reaches the acceptable electrical conductivity suggested by United Stated Department of Energy standard considered as material development. In particular, the findings of this research can be used to construct conductive electrodes particularly in oil and gas industries. [ABSTRACT FROM AUTHOR]

In this article, a model of a T-shaped fin, consisting of a set of ordinary differential equations (ODEs), is considered. The purpose of this article is to numerically solve ODE systems of a T-shaped fin (there is no reported exact and analytical solution) using an alternative approach. Utilizing a base approximation function, some mathematical principles and metaheuristics, an approximate solution very close to the existing numerical solution was found. The weighted residual function is used as an objective function along with its constraints, such as boundary and initial values. For the sake of comparison, a generational distance metric is used to evaluate the obtained results compared with the existing results in the literature. The approximate solution found by the applied approach demonstrates its efficiency and performance compared with the existing numerical approach. [ABSTRACT FROM AUTHOR]

In recent years, many researchers have utilized metaheuristic optimization algorithms along with fuzzy logic theory in their studies for various purposes. The harmony search (HS) algorithm is one of the metaheuristic optimization algorithms that is widely employed in different studies along with fuzzy logic (FL) theory. FL theory is a mathematical approach to expressing uncertainty by applying the conceptualization of fuzziness in a system. This review paper presents an extensive review of published papers based on the combination of HS and FL systems. In this regard, the functional characteristics of models obtained from integration of FL and HS have been reported in various articles, and the performance of each study is investigated. The basic concept of the FL approach and its derived models are introduced to familiarize readers with the principal mechanisms of FL models. Moreover, appropriate descriptions of the primary classifications acquired from the coexistence of FL and HS methods for specific purposes are reviewed. The results show that the high efficiency of HS to improve the exploration of FL in achieving the optimal solution on the one hand, and the capability of fuzzy inference systems to provide more flexible and dynamic adaptation of the HS parameters based on human perception on the other hand, can be a powerful combination for solving optimization problems. This review paper is believed to be a useful resource for students, engineers, and professionals. [ABSTRACT FROM AUTHOR]

In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses. [ABSTRACT FROM AUTHOR]

Minimum cost of energy is the main goal of a wind farm layout optimization. This is achieved by maximizing the total energy while minimizing the total costs of the farm. In this study, two sizes of commercial turbines were considered to investigate the effect of a non-homogenous farm on the layout optimization process. A cost model consisting of turbines, cable, transformers, foundation, and service vehicle routes was developed. Using Genetic Algorithm and Artificial Neural Network, first the superiority of the new algorithm in turbines and cable layout was verified versus previous studies. Next, two cases were investigated, i.e. (1) a farm populated with identical turbines and (2) a farm with a random mixture of both sizes of turbines. The layouts of both cases were optimized by both single and multi-objective optimizations. In the single objective optimization, only the larger turbines remained in the optimal layout of the second case and reduced the Levelized Cost of Energy (LCOE) into half of the first case. Multi-objective optimization clarified the reason for selecting larger size turbines in the layouts when the goal of the optimization was to minimize the cost of energy. As reported in literature, non-homogenous farms produce higher output. However, they impose a higher LCOE which makes them less appealing to developers. • Single objective (cost) and multi-objective (cost & power output) optimization result in different layout. • Non-homogenous farms produce higher power output. • Non-homogenous farms impose a higher LCOE which makes them less appealing to developers. [ABSTRACT FROM AUTHOR]

• This work studies an integrated distributed flow shop scheduling and distribution problem. • A mixed integer programming model is given to describe this problem mathematically. • An enhanced brain storm optimization algorithm is designed. • Optimal solutions are obtained by using mathematical programming solvers. • State-of-the-art results are acquired by the designed approach. Production and distribution are two essential activities in supply chain management. Currently, integrated production and distribution problems receive much attention because decision-makers devote to improving the operation efficiency of both stages and try to achieve an optimal solution. This work proposes an integrated distributed production and distribution problem with consideration of time windows, in which a set of jobs (i.e., customer orders) needs to be assigned among factories and the jobs are processed on flow shop environments at their associated factories. Then, the completed jobs are delivered by capacitated vehicles to customers in different regions while satisfying given time windows as much as possible. Accordingly, to optimally solve the proposed problem, a mixed integer programming model with minimizing total weighted earliness and tardiness has been established. For the optimization task, an enhanced brain storm optimization algorithm with some particular strategies is designed to handle the considered problem. To assess the performance of the proposed optimization method, several experiments by adopting a set of benchmark test problems are performed, and state-of-the-art optimizers are chosen for comparisons. The obtained optimization results exhibit that the designed algorithm significantly outperforms its rivals and can be considered as an excellent optimizer for solving the studied problem. Besides, compared with the CPLEX solver, the designed optimizer also performs much better for solving large-size problems. [ABSTRACT FROM AUTHOR]