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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 recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

In this article, a self-adaptive global mine blast algorithm (GMBA) is proposed for numerical optimization. This algorithm is designed in a novel way, and a new shrapnel equation is proposed for the exploitation phase of mine blast algorithm. A theoretical study is performed, which proves the convergence of any typical shrapnel piece; a new definition for parameters values is defined based on the performed theoretical studies. The promising nature of newly designed exploitation idea is verified with the help of multiple numerical experiments. A state-of-the-art set of benchmark problems are solved with the proposed GMBA, and the optimization results are compared with seven state-of-the-art optimization algorithms. The experimental results are statistically validated by using Wilcoxon signed-rank test, and time complexity of GMBA is also calculated. It has been justified that the proposed GMBA works as a global optimizer for constrained optimization problems. As an application to the newly developed GMBA, an important data clustering problem is solved on six data clusters and the clustering results are compared with the state-of-the-art optimization algorithms. The promising results claim the proposed GMBA as a strong optimizer for data clustering application. [ABSTRACT FROM AUTHOR]

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13-28 wt%), feed rate (1-5 mL/h), and tip-to-collector distance (10-23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results. [ABSTRACT FROM AUTHOR]

The heat sinks are utilized in electronic devices to eliminate heat from the chip and efficiently transmit it to the environment. Therefore, the optimal geometry sizes of fin heat sinks are the point of concern for manufacturers and designers. For this reason, the importance of optimization techniques particularly metaheuristics is understood. The design variables are width of heat sink, number of fins, fin height, and fin diameter. The various responses that have been considered are electromagnetic emitted radiations, thermal resistance, and mass of the heat sink investigated separately and simultaneously (multi-objective). Mine blast algorithm (MBA), as a recently developed optimizer, is inspired from explosion of mines. The optimum dimensions and values for each response have been obtained by the MBA and have been compared with other optimization methods in the literature. In terms of thermal resistance and mass responses, the MBA has offered better values, while for the emitted radiations, the obtained results obtained by Taguchi-based gray relational analysis (TGRA) was preferred. For manufacturing point of view, the MBA and TGRA both suggested better and efficient design. In addition, the value path analysis has been carried out to compare the trade-off among the considered responses. Finally, parametric sensitivity analyses have been implemented for design parameters, and discussions and comparisons have been carried out for the effects of each decision variable. By considering all responses, width of heat sink and fin height are considered as the most important and effective design parameters, respectively. [ABSTRACT FROM AUTHOR]

Injectable calcium phosphate cements have been introduced as adjuncts to internal fixation for treating selected fractures. These cements harden without producing much heat, develop compressive strength, and are remodeled slowly in vivo. The main purpose of the cement is to fill voids in metaphyseal bone, thereby reducing the need for bone graft. However, such cements may also improve the holding strength around metal devices in osteoporotic bone. This paper presents the optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin (CPC/MWCNT-OH/BSA) composites in terms of compressive strength using well-known metaheuristic optimizers. The process parameters studied were wt% of MWCNT-OH (0.2-0.5 wt%) and wt% of BSA (5-15 wt%). The obtained results from metaheuristic algorithms were compared with the results from the response surface methodology (RSM) in the literature. The results obtained from metaheuristic algorithms outperformed the results given by the RSM in terms of less error percentage and high compressive strength. [ABSTRACT FROM AUTHOR]

Selection of processing and geometrical parameters is a crucial step in the extrusion process design. Optimized parameters may result in desirable microstructure at minimum load. The purpose of this paper is determination of the optimal cold forward extrusion parameters with the minimization of tool load as the objective function. This paper deals with different optimization approaches in order to determine the optimal values of logarithmic strain, die angle, and friction with the purpose of finding the minimal tool loading obtained by cold forward extrusion process. The obtained extrusion force model as a fitness function was used to carry out the optimization. Based upon the objective function, metaheuristic algorithms such as genetic algorithm and simulated annealing were adopted as optimization methods for finding the optimum values of cold forward extrusion parameters and the obtained results were compared with those in literature. The better results lead to the smallest energy consumption, longer tool life, better formability of the work material, and the quality of the finished product. [ABSTRACT FROM AUTHOR]

Various metaheuristic optimization algorithms are being developed to obtain optimal solutions to real-world problems. Metaheuristic algorithms are inspired by various metaphors, resulting in different search mechanisms, operators, and parameters, and thus algorithm-specific strengths and weaknesses. Newly developed algorithms are generally tested using benchmark problems. However, for existing traditional benchmark problems, it is difficult for users to freely modify the characteristics of a problem. Thus, their shapes and sizes are limited, which is a disadvantage. In this study, a modified Gaussian fitness landscape generator is proposed based on a probability density function, to make up for the disadvantages of traditional benchmark problems. The fitness landscape developed in this study contains a total of six features and can be employed to easily create various problems depending on user needs, which is an important advantage. It is applied to quantitatively evaluate the performance and reliability of eight reported metaheuristic algorithms. In addition, a sensitivity analysis is performed on the population size for population-based algorithms. Furthermore, improved versions of the metaheuristic algorithm are considered, to investigate which performance aspects are enhanced by applying the same fitness landscape. The modified Gaussian fitness landscape generator can be employed to compare the performances of existing optimization algorithms and to evaluate the performances of newly developed algorithms. In addition, it can be employed to develop methods of improving algorithms by evaluating their strengths and weaknesses. [ABSTRACT FROM AUTHOR]