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This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and hai'monic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP. [ABSTRACT FROM AUTHOR]

Purpose: One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower. Design/methodology/approach: In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter. Findings: 12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems. Research limitations/implications: One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training. Originality/value: In this paper, a mathematical model was developed for multi-model assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NP-Hard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study. [ABSTRACT FROM AUTHOR]

The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented. Design/methodology/approach: In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. Findings: In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. Research limitations/implications: Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects: - Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters. - Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic. - To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. Practical implications: CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. Originality/value: In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid intelligent method is presented for detecting the bearing faults in the various rotating speeds. The vibration signals are collected in four conditions, including the normal state, the faulty inner race, the faulty outer race, and the faulty bearing element. Firstly, twenty-two statistical features in the time domain and four frequency features, three Wavelet packet decomposition (WPD), and the first five intrinsic mode functions obtained by the empirical mode decomposition (EMD) are extracted from the original signal; finally, the feature vector for each signal sample has 424 features. However, in the high dimensional feature matrix, there may exist the insensitive features to the presence of defects. Therefore, in this study, the compensation distance evaluation technique (CDET) is used to select the optimal features. Then, the selected features are used as the inputs of the support vector machine (SVM) classifier to diagnose the bearing conditions. In the CDET method, there is a threshold indicator that plays a decisive role in choosing the desired attributes. Also, the SVM method has some parameters that need to be set during the fault detection process. Therefore, the particle swarm optimization (PSO) algorithm is used to determine the optimal threshold in the CDET method and the optimal SVM parameters, so that the prediction error of the bearing conditions and the number of the selected features are minimized. The obtained results demonstrate that the selected features are well able to differentiate between different bearing conditions at various speeds. Comparing the results of this paper with other fault detection methods indicates the ability of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model for increasing the precision of the support vector machine classifier is proposed to detect stator windings short circuit fault detection in induction motors. The proposed method consists of three different phases, wherein the first phase the statistical features of a healthy and defective data set are extracted. The principal component analysis is used to reduce the dimensions of the obtained features. Then, different SVMs are constructed based on training data sets. To achieve a better result, the parameters of the SVM are determined by the fractionalorder chaotic particle swarm optimization algorithm. Finally, a hybrid model for combining SVMs with type-2 Fuzzy logic is implemented. The proposed approach is then applied on measured stator current data for stator winding short circuit fault detection in a three-phase induction motor with 2.2kW, 50Hz, 6 Pole. The average accuracy of 98.4% of the detection of stator winding error on laboratory data under different load conditions indicates the performance and validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the most important geophysical problems in exploration of mineral deposits is to estimate the depth of buried structure using observed gravity data. In this paper, we are trying to estimate mass anomaly depth by using one of the intelligence methods as Particle Swarm Optimization (PSO) with simple shapes as sphere, horizontal and vertical cylinder. In this modeling, two parameters of depth (z) and shape factor (q) were considered as particles and the maximum and minimum depth were used as the prior information. The method is tested for synthetic models with random noise. The method gives precise results for synthetic models contaminated with random noise which is quite acceptable and promising. This technique was also successfully applied to real data for mineral exploration. The applied real data belongs to an area with hilly topography located in the Fars province close to the Abadeh city where the barite deposit is under exploration. The method is used for a profile of real data that is provided from the residual anomalies and passed from the main detected positive anomaly in the area. The estimated depth from this method was 8.15 m which was in good agreement with the results obtained through Euler method and also outcrop-scale observations. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology significantly simplifies the production of complex 3D parts directly by the computer-aided design model. However, additive manufacturing processes have unique flexibility. They still have restrictions that don’t allow engineers to generate some specific geometric shapes, easily. Some of these restrictions are the consumption of materials to supports, the poor abrasion resistance and the inferior surface finish of some surfaces with certain angles. One of the methods to overcome these problems is designing by segmentation. The proposed methodology consists of two steps: 1) segmenting of the 3D model and 2) exploring the best orientation for each segment. In the first step, engineers consider the possible number of segments and the connection method of segments. In this paper, a series of segments is obtained by recognition of features and separating them with one or more appropriate planes. In the second step, the best fabrication orientation should be chosen. The criterion for optimization is that the support volume, abrasion ability, and surface roughness should be minimum. The operation is performed automatically by the algorithm created based on principles of the Particle swarm optimization algorithm using visual C#. Experimental tests show that segmentation design improves additive manufacturing processes from the aspects of material consumption, abrasion volume, and surface quality. This paper presents an original approach to improving the efficiency of additive manufacturing technologies that make the additive manufacturing closer to maturity. [ABSTRACT FROM AUTHOR]

In this paper, one-dimensional numerical optimization of lead-acid battery with finite-volume method is performed using the governing equations of battery dynamics. For validation, the present results are compared with previous studies which show good agreement. The demand for batteries with high energy and power has increased due to their use in hybrid vehicles. The major shortcoming of lead-acid batteries in industry is low energy and high weight; therefore,ƒcell with higher energy and lower thickness is designed by using particle swarm optimization based on developed simulation code, which is less time consuming and much faster than experimental method. The results of optimization show that an optimal battery that has 85 percent higher energy can be made with the same cell length. The results also show that an optimum cell battery can be obtained with a 25 percent decrease in weight and 23 percent in dimensions while keeping the energy content constant. [ABSTRACT FROM AUTHOR]

Purpose: The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented. Design/methodology/approach: In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. Findings: In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. Research limitations/implications: Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects:Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters.Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic.To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. Practical implications: CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. Originality/value: In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines.

A water distribution network (WDN) may not be able to satisfy all required demands when it’s in the pressure deficit mode or under over-loaded demand conditions. Analysis of the network in this mode requires pressure dependent analysis (PDA). Unlike demand driven analysis (DDA), PDA needs an extra equation for every node to relate the nodal demand and the nodal pressure; so it should be solved with the other network’s equations simultaneously. In this paper, based on the Particle Swarm Optimization (PSO) algorithm, a decision support system has been developed by using MATLAB and EPANET for PDA simulation in WDNs. A four-loop network selected from the literature was analyzed using different scenarios and different pressure dependent functions presented by the previous investigations. The results showed that the proposed model (PSO-PDA) was as accurate as the previous ones and provided better convergence. The results of the nodes’ pressure and discharge also indicated minor differences obtained by different PDA functions. However, the differences between the results of PDA and DDA were considerable. [ABSTRACT FROM AUTHOR]

The use of the electric energy stored in batteries of electric vehicles connected to the grid (V2G) will play a significant role in the development of distribution systems in the future. Electric vehicles (EVs) are able to charge during base load hours and inject energy into the grid during peak hours. Besides, the grid-connected EVs increase the system reliability under the outages. In this paper, Particle Swarm Optimization (PSO) algorithm is employed to solve the multi-objective problem of development scheduling of electric vehicles in the distribution network. ε-constraint method is employed to solve the proposed multi-objective problem. Besides, a fuzzy decision making approach is employed to determine the most compromise solution among the Pareto solutions obtained by the solving the sub-problems generated by the ε-constraint method. Decision variables include the location, and charge and discharge capacity of the smart parking lots of the EVs. IEEE 54-bus distribution test system is employed as the studied test network. Operation costs of the distribution network are compared in both states of with or without EVs. The results demonstrate the effectiveness of the proposed method for EVs' development scheduling in the distribution network. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry. Design/methodology/approach: To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities. Findings: Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy. Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable. Originality/value: This study was one of the first in-house studies to compare the results of metaheuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers. [ABSTRACT FROM AUTHOR]

In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values ​​of the parameters in the torque controllers have not converged to their true values ​​due to the large modelless dynamics, while they have converged to their true values ​​in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

Problems of water delivery scheduling are one of the reasons for poor performance of the Optimization operation of water resources projects. These problems lead to high operational losses and miss-match of water delivery with water requirements. A great Number of studies have been conducted in quest of suitable methods capable of handling such problems. In recent years, some new heuristic methods such as particle swarm optimization and Genetic Algorithms have been introduced in systems engineering. Preliminary applications of these methods in water resources problems have shown that some of them are powerful tools, capable of solving complex problems. In this paper, the application of such heuristic methods as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has been studied for optimizing reservoir operation. The fitness function is minimizing the difference between monthly release and downstream demand for water with regard to establishing the continuity equal was selected. The methods were applied and compared using short-term (one year) model. Comparison of the results showed that PSO than GA is better capability in finding global optimum Solutions and operating curves. [ABSTRACT FROM AUTHOR]

Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN) and evaluate it in order to predict the dose patients. Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC) candidates for warfarin anticoagulant therapy within the international normalized ratio (INR) therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO) and evaluation function neural networks in MATLAB (MathWorks, MA, USA), were performed. Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) were 0.0262, 0.1621 and 0.1164, respectively. Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system. [ABSTRACT FROM AUTHOR]

Recently, providing a model in order to investigate human body vibrations and also finding a methodology to simulate the functionality of the central nervous system in muscle tuning while pre and post fatigue conditions during running has been discussed in numerous papers. In the current study, a 4 degree of freedom vibration model of human body and an objective function that mimics the functionality of the central nervous system with bounds for pre-fatigue and post fatigue conditions were considered. Firstly, it was shown that the optimization method used in the literature leads to inaccurate results. Then, in order to achieve more accurate results, the optimization problem was solved using Particle Swarm Optimization method. A comparison between the results of the current study with those in literature showed that our results are more consistent with the experiments. Our findings indicated that an increase in running time followed by an increase in fatigue leads to a significant decrease in the safe region and increase in vibration amplitude of lower body wobbling mass. Moreover, a parametric study was conducted to investigate the effects of mass distribution and variations in touchdown velocities on the area of the safe region for both pre and post fatigue conditions. High dependency of this area to the mass distribution and touchdown velocities of lower extremities was also indicated. The results of this study can be useful in the shoe design for running and similar activities. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical modeling of nested hierarchical p-center problem is presented. In this model there are two levels of hierarchies and the first one should be passed to achieve the second level . The second level facility can provide both services of the first and the second level. This model is a linear integer mathematical programming model. To provide an application of the model, some simulated cases are investigated and resolved, the results show that using this model reduces the initial construction cost while the main objective which is maximum servicing distance will not be changed basically.for the large size problem using of metaheuristic algorithms is necessary so in this paper an algorithm based on the Particle swarm intelligence is proposed to solve the problem. Numerical results confirm efficiency of the algorithm.

Electroencephalogram signals (EEGs) show the electrical activity of brain neurons. EEG is a non-invasive method that can be used to detect abnormal brain activities. Seizure is one of these abnormal activities and is the most common manifestation of epilepsy. Spikes are the most important characteristic of the seizure prone EEG signals. By detecting spikes, it is possible to detect epileptic seizures from EEG signals. EEG signals are non-stationary signals, so the wavelet transform that has appropriate time and frequency resolution can be a good option for extracting features of EEG signals. In this paper, after the extraction process using wavelet transform, artificial neural networks (ANNs) are used to classify healthy and epileptic signals. Particle swarm optimization (PSO) is also used as a novel approach to select weights and biases of network to improve network performance. The results of the implementation of the proposed algorithm have a 96.2% accuracy, which shows acceptable performance compared to existing methods. [ABSTRACT FROM AUTHOR]

Purpose: The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while noticing travel and passenger transportation costs and use modeling compatible with this field to optimize goal achievement. Methodology: The problems of optimizing hotel revenue management, passenger cost management, and analyzing how to expand the transportation used by them have been studied in this research. One of the key issues studied is to predict how to transport a passenger and choose its type based on different modes of travel such as air, rail, water, and road based on the amount of the passenger’s budget. Findings: Many effective factors and criteria have been considered in the modeling done, and the amount of hotel reception capacity in the selected cities of travelers and the provision of various types of rooms with different pricing, and the examination of elements related to the services provided to travelers by the hotel and different accesses of the hotel, which is based on the hotel’s revenue model, affect on. It is useful to estimate the state of competitive factors of hotels. Noteworthy, the transfer and mode of transportation have been determined to predict the level of demand for hotel reservations for all types of travelers during different periods in different tourism seasons. This subject is based on the traveler’s budget allocated for paying expenses during the travel pattern and the related results extracted from the estimated income model, as well as the influencing factors in choosing the hotel and transportation. Originality/Value: In the current study, the design of NP-Hard problems led to the use of exact methods in smallsized problems and two multi-objective meta-heuristic algorithms, namely NSGA-II and MOPSO, in medium- and largesized problems. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving. [ABSTRACT FROM AUTHOR]

In this paper a multi-commodity multi-period inventory routing problem in a two-echelon supply chain consisting of a manufacturer and a set of retailers has been studied. In addition to inventory management and distribution planning, production planning has also been considered in the above problem. The objective is to minimize total system cost that consists of production setup, inventory holding and distribution costs. The commodities are delivered to the retailers by an identical fleet of limited capacity vehicles through direct shipment strategy. Also it is assumed that production and storage capacity is limited and stockout is not allowed. Since similar problems without distribution planning are known as NP-hard, this is also an NP-hard problem. Therefore, in this paper, a new improved particle swarm optimization algorithm has been developed consisting of two distinguished phases for problem solving. First, the values of binary variables are determined using the proposed algorithm and then, the continuous variables are calculated by solving a linear programming model. Performance of the proposed algorithm has been compared with genetic and original particle swarm optimization algorithms using various samples of random problems. The findings imply significant performance of the proposed algorithm.

This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and harmonic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP.

The accurate and fast localization in the Internet of Things is an essential requirement in expanding the use of applications for these networks. Due to limited computing power of sensor nodes and the limited power capacity of these nodes, reducing computational complexity and reducing communication overhead in localization algorithms is critical. In this paper, a particle swarm optimization based algorithm is proposed for localization of sensor nodes in IoT. In general, trapping in local optima and the slow convergence rate are two main weaknesses of the classic PSO. In the proposed algorithm, using the chaos theory, trapping in local optima is prevented and convergence of the algorithm is improved. In addition, using the fractional derivatives, the particles convergence rate accelerates to the optimal solution. The performance evaluation of the algorithm is performed by its implementation on specific test functions. Simulation results exhibit the effectiveness of the proposed algorithm in localization of sensor nodes in IoT.

Today, business process management systems (BPMS) are rapidly expanding. Organizations and corporations need to leverage BPMS to manage their business processes and to increase economic productivity for competing in the international market. Therefore, researchers are to increase the features of BPMS. Workload balancing in BPMS is one of the challenges which have been studied by researchers. The purposes of workload balancing include increasing system stability, improving the resource efficiency and enhancing the quality of its products. In this paper, undesirable effects of the time delay of arrival rate in the tasks are presented. To overcome the undesirable effects, we define the workload pattern of resources and we propose a heuristic method to manage and regulate the processes in the BPMS as Process Concurrency. we leverage PSO algorithm to optimize process concurrency. Therefore, in addition to balancing the workload of resources, workload for each resource is maintained uniform through time. Creating uniformity in workload, as a result, increase the resource efficiency and improve the quality of products and services. To evaluate the proposed method, experimental results showed the optimal process Concurrency 29.8% increase balance and uniformity in the resource workload.

Believing in the fact that an activity can be performed through several methods or states, or that there may be several executive states for activities with different numbers of resources in real conditions, the scheduling obtained from different methods with different combinations of time, resources, and quality need to be evaluated and optimized. These are referred to as project scheduling problems with multistate resources. On the other hand, application of the theory of constraints to project management has led to the development of a novel approach in project planning and control known as the critical chain. The present paper seeks to use the critical chain technique and simultaneous time-cost-quality trade off. In this study, the multi-objective problem of time-cost-quality trade off has been solved using the high capability of the multi-objective particle swarm algorithm in optimization and the critical chain technique, so that the most appropriate sequence and executive state of activities is found such that time, cost, and quality are optimized and close to reality. The optimization algorithm programming was coded in the MATLAB software, and the target results were extracted. For evaluation of the proposed model, two case studies with 7 and 18 activities have been solved. Furthermore, a project with 60 activities where the time-cost-quality trade off had been established were used for validation of the proposed algorithm, and new results were extracted. The results demonstrated that the developed algorithm has had correct, proper performance, in such a way that it is capable of generating several Pareto solutions with different time, cost, and quality values. Furthermore, the results have been reported separately based on the best time, cost, and quality, which makes it possible for the project managers to select the better solutions given their priorities in the project.

A water distribution network (WDN) may not be able to satisfy all required demands when it’s in the pressure deficit mode or under over-loaded demand conditions. Analysis of the network in this mode requires pressure dependent analysis (PDA). Unlike demand driven analysis (DDA), PDA needs an extra equation for every node to relate the nodal demand and the nodal pressure; so it should be solved with the other network’s equations simultaneously. In this paper, based on the Particle Swarm Optimization (PSO) algorithm, a decision support system has been developed by using MATLAB and EPANET for PDA simulation in WDNs. A four-loop network selected from the literature was analyzed using different scenarios and different pressure dependent functions presented by the previous investigations. The results showed that the proposed model (PSO-PDA) was as accurate as the previous ones and provided better convergence. The results of the nodes’ pressure and discharge also indicated minor differences obtained by different PDA functions. However, the differences between the results of PDA and DDA were considerable.

Nowadays the effort estimation of software development is crucial in Software projects management. Not only have the accurate estimate of cost help customers and investors, but also it will be effective in rational decision-making in the implementation and management of software projects. Various estimation models have been invented and used so far. Many of the current effort estimation approaches are adopted by collecting data from previous projects. Case-based reasoning (CBR) is one of the successful techniques of effort estimation in software projects. This method alone is not very accurate, a defect which can be corrected by creating hybrid models. In this paper, CBR was combined with two separate metaheuristic algorithms including particle swarm optimization (PSO) and the firefly algorithm to propose a new hybrid model. Then the performance of the proposed model was evaluated. According to the results of the proposed model on Cocomo, Albrecht and Maxwell datasets, the firefly algorithm showed an acceptable performance.

The main aim of this paper is to provide a model for supply chain design of decorative stones in competitive conditions by taking into account the risk of the disruption. The main tools used in this study are mathematical programming and game theory which Stackelberg game is often used for competitive modeling. The aim of the proposed model is to consider potential risks of disruption and competitive situation in the market. Also, due to the problem situation, we use meta-heuristic methods like particle swarm optimization and biogeography based optimization for solving and verification of the model. The results show that the occurrence of disruption affects in location decisions of production and distribution centers, price of products and profit of chain components

This paper presents a comprehensive model for dynamic expansion planning of distribution grids (DDGEP) considering distributed generation technologies. The proposed model determines the optimal location, capacity and dynamics (i.e. timing) of DG investment as well as optimal time schedule of reinforcement of distribution feeders. The objective function of this model encompasses both investment and operation costs of distribution grids and DG units along a specified planning horizon. To solve the suggested model, a new two-level solution method composed of Binary Enhanced Imperialist Competition Algorithm (BEICA) and Improved Particle Swarm Optimization (IPSO) is introduced. BEICA optimizes the location, capacity and timing of DG investment and also timing of existing feeders' reinforcement while IPSO optimizes the operation point of distributed generator-integrated distribution system. In order to demonstrate the effectiveness of proposed two-level solution approach (BEICA+IPSO), it is applied on a radial distribution test system and the obtained results are compared with several other solution methods.

In this paper presented the evaluation of particle swarm optimization for solving complex optimization water resources problems. The main problem with PSO is it’s prematurity. Therefore a new adjustable PSO-GA hybrid algorithm which combines PSO with genetic operators was proposed. The basis behind this is that such a hybrid approach is expected to have merits of PSO with those of GA. The main idea of GA is due to its genetic operators crossover and mutation. By applying crossover operation, information can be swapped between two particles to have the ability of flying to the new search area. The purpose of applying mutation to PSO is to increase the diversity of the population. For evaluating of the proposed algorithm the optimization of the hydropower operation of ‘‘Dez” single reservoir has been studied. The results of HPSOGA compare to PSO and GA indicated the proposed algorithm increases the flexibility and capability of PSO to generate strong-developing individuals that can achieve faster convergence rate to optimum point and it is very useful in solving optimization operation water resources.

According to the Environmental Kuznets curve hypothesis, the relationship between per-capita GDP and per-capita Pollutants has an inverted U-shape. Most studies on this subject are based on estimating fully parametric quadratic or cubic regression models. The purpose of this paper is to simulate the relationship between per-capita carbon dioxide (CO2) emission and per capita income in Iran using genetic algorithm and Particle swarm optimization algorithm concerning three functional forms (linear, quadratic and exponential). Investigating the forecasting accuracy criteria the most subtle model is used to forecast per-capita Co2 emission up to 2025 concerning five scenarios. More minuteness of GA, choosing exponential form as the most subtle functional form, positive effect of Fossil fuel energy consumption and negative effect of economic growth on Co2 emission are the main results.

In this paper a novel mathematical model has been proposed for multi-objective scheduling problem in a cellular manufacturing system (CMS) with the aim of minimizing the maximum completion time of jobs (i.e., makespan or Cmax), the earliness cost and the tardiness cost. Due to the complexity of such a hard problem, a hybrid algorithm, based on a genetic algorithm (GA) and particle swarm optimization (PSO), has been proposed to solve the presented model in a reasonable computational time. Furthermore, non-dominated sorting genetic algorithm (NSGA-II) as a well-known multi-objective evolutionary algorithm has been used to analyze and highlight the efficiency of the proposed hybrid algorithm. The associated results of the algorithms have been compared and analyzed. Finally, conclusions have been made and suggestions for further study have been presented.

High strength to weight ratio is significant specific of sandwich panels due to trait particular of special structure of them. The variables that defined for design of sandwich panels should be determined with procedure that it has lowest weight with appropriate strength. Due to this reason, utilization of optimization algorithms to design of these panels has a particular importance. Particle swarm optimization (PSO) algorithm is a random method that is adopted from nature for solving couple and multi objective problems in engineering. In this paper the application of this method in weight optimization of prismatic core sandwich panels based on yield and buckling criteria is proposed. Because of the PSO algorithm, doesn't need the derivative and special preliminary guess and also is a random procedure, this algorithm is able to search the extended solving space with appropriate probability. Comparison of results represented that PSO algorithm in few iterates can achieve to better results.

The echoes obtained from ultrasonic testing of materials contain valuable information about the geometry and grain structure of the test specimen. These echoes can be modeled by Gaussian pulses in a model-based estimation process. For precise modeling of an echo, the parameters of the Gaussian pulse should be estimated as accurately as possible. There are a number of algorithms that can be used for this purpose. In this study, three different algorithms are used: Gauss-Newton (GN), particle swarm optimization (PSO), and genetic algorithm (GA). The pros and cons of each of these three algorithms are reviewed and by combining them, the benefits of each algorithm are used while its shortcomings are avoided. For signals containing multiple echoes, the minimum description length (MDL) principle is used to estimate the numbers of required Gaussian echoes followed by space alternating generalized expectation maximization (SAGE) technique to translate it to separate echoes and to estimate the parameters of each echo. The performance of the proposed algorithms for simulated and experimental signals with overlapping and non-overlapping echoes is evaluated and is shown to be quite effective. [ABSTRACT FROM AUTHOR]

In this study a safe and smooth path planning containing the slightest risk is considered for an Unmanned Underwater Vehicle (UUV). To do so, three smooth and continuous functions resembling the three dimensional path are introduced and then their parameters are optimized using the particle swarm optimization method to find the safest possible path. For each point in space a numeric value is considered as vulnerability and the objective function is the integral of the vulnerability over the path produced. This path forms controlling signals which, through a TSK fuzzy controller, the UUV is guided. The new arrangement of the propulsion vehicle subsurface was modeled. Since for the design of the controller the parameters of the Under Water Vehicle dynamic system is not used, the control system is robust with respect to parameter uncertainties. In the last section three environments with different complexities are considered to illustrate the creating process's performance of the path and it is concluded that this method demonstrates desired performance in the development of a safe and smooth path through a harmful environment and design of an adequate controller. [ABSTRACT FROM AUTHOR]