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This article studies the numerical solution of partial differential equations by Multi Agent Simulation (MAS). MAS is often used to simulate social phenomena such as traffic flow and evacuation behavior. MAS consists of agents acting autonomously and a space in which agents act. Each agent perceives its surrounding environment and interacts with each other. By the interaction, a macro phenomena emerge in the whole space from a micro one of independent agent of each agent. In order to extend the application fields of MAS technology, several works have been conducted. Among them, our research group has focussed on solving methods of linear simultaneous equations by using particle swarm optimization which is a method applying the concept of MAS. As described above, MAS is currently used in various fields and is expected to be an active field of research in the future. This study developed the method to solve partial differential equation numerically by MAS. In addition, a modeling of Stefan problem which is difficult to analyze by differential method is proposed in this study. Stefan problem is used not only in the freezing of ice, but also in various fields such as metal casting and plate tectonics theory of the earth science field, and it can be applied to various application fields when it is easily solved by MAS. This paper aims to investigate new application fields of the usage of MAS, we focus on the physical phenomena which constitute partial differential equation and propose the solution of that using the concept of MAS. Our method does not require the mathematical modelling but we solve using physical model directly. [ABSTRACT FROM AUTHOR]

Knowing the position of the interesting object has gotten much attention from many researchers due to the opportunity to develop myriad applications. In this paper, we will integrate all the processes required as a process that can be used to estimate the unknown object's position. First, we measured a real-time signal strength (SS) received by the unknown node simultaneously from three anchor nodes. We use the Tmote sky node as transceivers. Second, we estimate the distance of an unknown node from the anchor nodes based on the path loss model; third, we use the linear least square estimation, gradient descent (GD), and particle swarm optimization (PSO) to estimate four different unknown node position (P1, P2, P3, and P4). We found that all algorithms are sufficient to estimate the node position even with some error in the distance estimation obtained except that linear least square got the worst position estimation. A linear LSE does not minimize the error of unknown node position estimation from all anchor nodes. We found that the PSO algorithm is better than GD algorithms regarding time execution and gives an opportunity to be implemented in real-time hardware execution. [ABSTRACT FROM AUTHOR]

Robotics is among the much-touted treatment and therapy solutions in modern dentistry. A unique Particle Swarm Optimization PSO is utilized to manage the needle tip location and direction as a function of the knots along the path between the end-effector segments in a dentistry robotics navigation system recently suggested. The innovative "Numeric Alphabet Flow" (NAF) method may control any point-to-point surgical motions for a three-link robotic guiding system. The new NAF approach creates a brand-new form known as the NAF-PSO methodology by using flowing values and alphanumeric characters in the fuzzy rules and employing particle swarm optimization techniques. Within that paper, we use the novel NAF-PSO to improve robotic tracking performance. This study introduces a unique route planner that specifies the suggested NAF-PSO function as minimizing space and traveling time while not reaching a maximal preset force and avoiding obstacles. [ABSTRACT FROM AUTHOR]

Metaheuristic Algorithms have gained remarkable attention in the past years due to their capability of optimization. These algorithms tend to provide near-optimal solutions to complex computational problems. Nature-inspired metaheuristic algorithms take inspiration of their approach from natural processes. In this paper, we have studied 3 nature-inspired metaheuristic algorithms: Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization. Artificial Neural Networks are adaptive models that have the capability to learn from the past mistakes and keep improving on a continuous basis. These mimic the biological neuron network in a human brain. However, there is always a need to optimize these neural networks. Many tasks such as parameter selection, data training etc. are major challenges in ANNs. In this study, we see how numerous researchers have applied metaheuristic techniques to ANNs in order to tackle these problems. [ABSTRACT FROM AUTHOR]

Metal matrix composites are widely used in number of engineering applications due to its improved qualities over the conventional materials. This paper aims in optimization of machining responses like surface roughness (Ra), cutting force (Fz), in turning of AA7068/TiC MMCs using particle swarm optimization technique. The control parameters aken into account are wt. % of TiC, cutting speed (V), feed rate (f), depth of cut (d). Experiments were designed and carried out using central composite design (CCD) in design of experiments (DOE) on a lathe machine with three different types of cutting tool inserts viz. KC5410, KCP05, and K30. Quadratic predictive model was developed for the machining responses and their adequacy has been check through ANOVA. [ABSTRACT FROM AUTHOR]

Conjugate Gradient (CG) methods are very popular methods that have been applied by many researchers for solving unconstrained optimization problems. Recently, many efforts have been done in order to improve the efficiency of this. For unconstrained optimization, line search act as a pillar for solving optimization problems. This paper proposed a new modification of approximate inexact line search in CG methods. It is based on classical bisection line search method. Recently, new modification of bisection named as n-th section method has been proposed. However, n-th section method inherits the same behaviour as bisection method which is slow convergence. Thus, a re-modification on n-th section algorithm is conducted to reduce its CPU time per execution. Overall, six line search methods that are employed in CG methods which are classical bisection, 4th section, 6th section, modified bisection, modified 4th section and modified 6th section are compared through performance profile analysis based on number of iterations and CPU times. These line search methods are tested based on six standard optimization test problems. The CG methods used in this research are classical formulas known as Fletcher-Reeves (FR), Polak-Ribiere-Polyak (PRP) and Rivaie-Mustafa-Ismail-Leong (RMIL). Numerical results shown that modified 6th section line search is the best method in term of number of iterations while modified 4th section is the best method based on CPU times. Other than that, the modified version of n-th section line search method has less amount of CPU time allocated to execute CG's algorithm when compared to the original version of n-th section line search method. The performance profile shown that the modified n-th section line search method is more efficient compared to the classical bisection line search in CG method. [ABSTRACT FROM AUTHOR]

In this paper a Particle Swarm Optimization (PSO) algorithm is considered in order to optimize the triangular Membership Functions (MF) in a Fuzzy Logic Controller (FLC). PSO algorithm belongs to the class of Swarm Intelligence (SI) techniques and is considered an efficient heuristic technique for optimization problem in a continuous and multidimensional search spaces. Performance of a FLC depends on the fuzzy partition of each input/output space considered and the PSO algorithm can be used to obtain the optimal or near optimal parameters of the triangular membership functions in order to achieve the best results in the defuzzification process. Simulation results obtained by this approach to tune the triangular membership functions of a FLC for an application concerning the optimization of the energy consumption in Industrial Wireless Sensor Networks (IWSN) are reported. [ABSTRACT FROM AUTHOR]

As uncertainty essentially affect the total transportation cost, it remains important in the container terminal that incorporates several modes and transshipments process. This paper then presents a stochastic discrete optimization model for designing the container terminal, which involves the decision of facilities improvement action. The container terminal operation model is constructed by accounting the variation of demand and facilities performance. In addition, for illustrating the conflicting issue that practically raises in the terminal operation, the model also takes into account the possible increment delay of facilities due to the increasing number of equipment, especially the container truck. Those variations expectantly reflect the uncertainty issue in the container terminal operation. A Monte Carlo simulation is invoked to propagate the variations by following the observed distribution. The problem is constructed within the framework of the combinatorial optimization problem for investigating the optimal decision of facilities improvement. A new variant of glow-worm swarm optimization (GSO) is thus proposed for solving the optimization, which is rarely explored in the transportation field. The model applicability is tested by considering the actual characteristics of the container terminal. [ABSTRACT FROM AUTHOR]

Because of the standard particle swarm optimization algorithm (Particle Swarm Optimization PSO) has slow convergence speed and easy to fall into the local minimum problem, in order to solve these problems, this paper proposes a multi-objective particle swarm optimization method based on improved culture. The simulation results show that the proposed algorithm is better in convergence speed and local minimum value, which shows that the improved particle swarm optimization algorithm has a good reference value [ABSTRACT FROM AUTHOR]

This paper proposed a meta heuristic Particle Swarm Optimization to solve unequal area for optimization problem at unstable demand of market.The paper proposed a reconfiguration of manufacturing facility to ensure optimum Material Handling Cost (MHC) and find flexible routing of parts.First a mathematical model was established with constrained, which used a triangle condition to ensure the integrity of cells.Then the natural inspirations particle swarm algorithms as metaheuristic optimization were applied on case study used for literature used seven machines with three products. The simulation shows good results and flexible flow of parts.The the cost of movements for the part (k) is directly related to the Objective Function Value as it discustred in conclusion. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a stochastic computational technique for finding optimal regions of complex search spaces. The method was inspired by social behavior of organism and it belongs to the evolutionary computation (EC) to the group of swarm intelligence (SI). It has a few parameters to adjust and the method is easy to implement, which are the main advantages. The main aim of this paper is to find out, if limitation of the search space influences the optimization process. In this paper, we compare two modifications of PSO algorithm - variant using the parameter of inertia weight and variant with constriction factor. Both variants are solved separately for constrained and unconstrained space. Constrained area is when the boundaries are clear district, unconstrained is when particles can cross the boundaries. For comparison, eleven benchmark functions which were prepared for the special session on real-parameter optimization of CEC 2005 were used. Differences in results between constrained and unconstrained area is presented in this paper. It was found, that constrained area of search space gives better results. In additional, variant of PSO algorithm with linearly decreasing inertia weigh is better for chosen optimization problem. [ABSTRACT FROM AUTHOR]

Power consumption has become an important issue in embedded system. Reducing the total number of signal transitions during each bus cycle has great effect on power consumption. In this paper, we proposed an instruction scheduling method which is based on the Particle Swarm Optimization (PSO) Algorithm to reduce the signal transitions. We formulate the low-power instruction scheduling problem to the constrained discrete PSO problem. And then we modify the PSO solution to satisfy the path constrained and find the better instruction schedule with litter signal transitions. For more effectively, we also improve the velocity updating formula to get the better result. The main contribution of our paper is that we use the improved PSO algorithm to find the better instructions sequence for low power quickly. [ABSTRACT FROM PUBLISHER]

In this paper, a new registration method that based on shape information and gray information has been introduced to the procedure of CT-MRI registration. In the first, the frames of the two images to be registered were explained by the principle of mechanics decomposition, then we coarsely registered the two images by their frames and as a result the error of the registration was limited in a small region. In the second step, we re-registered the two images that have been coarsely registered by means of maximum mutual information (MMI), and finally an accurate registration result was obtained. The simulation result shows this method is less time consuming and on the other side the registration accuracy is also guaranteed. Finally we successfully applied the present method to register the CT images and MRI images in a patient undergoing neurosurgery. [ABSTRACT FROM PUBLISHER]

Distributed generation is the process of generating electricity from small energy sources and connecting it to the distribution network or on the customer's side of the meter. The placement of distributed generation and the size of the distribution generation is important. Determining the optimal location and sizing of a Distributed Generation (DG) Unit is being presented in this paper. The radial distributed network has been considered. A robust stochastic optimization technique based on the movement and intelligence of swarms called Particle Swarm Optimization has been implemented to obtain optimal solution of the DG placement problem. Reduction of line losses in the radial distribution network is the main objective of this paper. [ABSTRACT FROM PUBLISHER]

This paper present a method to determine the optimal tuning of the PI controller parameter on Direct current (DC) motor drive system using particle swarm optimization (PSO) algorithm, Ziegler-Nichols (ZN) tuning and Modified Ziegler-Nichols (MZN) tuning method. The main objective of this paper is to minimize transient response specifications chosen as rise time, settling time and overshoot, for better speed response of DC motor drive. The speed control of DC motor is done using PI and PID controllers. Implementation of PID controller for DC motor speed control is done using ZN and MZN tuning method. For PSO algorithm technique, PI controller is used to improve the performance of DC motor speed control system. A comparison is made on the basis of objective function (rise time, settling time and overshoot) from output Step responses. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PI controller parameters yields high-quality solution. Compared with traditional ZN method and MZN method, the proposed method is found indeed more efficient and robust in improving the step response of DC motor drive system. [ABSTRACT FROM PUBLISHER]

With the background of satellite module layout, This paper combines heuristic strategy with the glowworm swarm optimization for the problem of rectangle layout optimization with equilibrium constraint. We use heuristic strategy to divide the circular container into four sub-regions, which are layout synchronously. When the rectangle layout area has been decided, the bottom left fill strategy is used to layout the rectangle. On the basis of the heuristic strategy, glowworm swarm optimization algorithm is applied to search for the optimal placing order, and finally the optimal layout is obtained. The simulation numerical results show that this paper algorithm is more effective than the existing algorithms. [ABSTRACT FROM PUBLISHER]

PF (Particle Filter) is getting prevailing in visual tracking. Nevertheless, PF may cause particle leanness and divergence easily because the observation equation is restricted by the utilization of the observed data. According to the specificity of the forward-looking sonar images and the demand of underwater visual tracking for AUV (Autonomous Underwater Vehicles), this paper forms a tracking method based on PSOPF (Particle Swarm Optimized Particle Filter) which fuses PSO (Particle Swarm Optimization) into PF. PSO drives the particles to high likelihood region by updating their position and speed. MAD (Mean of Absolute Differences), the typical correlation operator, is used as the fitness. In order to control the optimization, this paper proposes a global fitness to describe the particles' global distribution using the peak width at half height around the global extremum. Experimental results show that comparing with the traditional PF, the PSOPF has better robust performance against multi-path disturbance, which makes the PSOPF suit for underwater visual tracking for AUV. [ABSTRACT FROM PUBLISHER]

In the vehicle routing problem (VRP), it is usually difficult to confederate the cargo and arrange vehicles path. Due to performance reasons, the traditional shortest path algorithm can not be applied to the large scale of VRP. On the basis of the VRP mathematical model, this paper constructs a mixed climbing particle swarm algorithms to solve the problem. First, through coding, the VRP problem is divided into two sub-problems: task allocation and single vehicle path optimization. Particle swarm algorithm is in charge of controlling the overall situation and allocating task, while hill-climbing algorithm is responsible for calculating the vehicle path optimization (fitness). Finally, by performing experiments in MATLAB programming and comparison of the operational results of the matrix method and genetic algorithm, the algorithm is shown to be feasible in solving VRP and have higher practicability. [ABSTRACT FROM PUBLISHER]

This paper presents an optimal controller design of cascade PI controllers for a buck converter using both particle swarm optimization and adaptive tabu search methods. The averaging model of the power electronic system derived from the generalized state-space averaging method is used as an objective function instead of the model from software packages. The simulation results show that the proposed method can efficiently obtain the best performance of the output waveform compared with the classical method. Moreover, this approach is convenient and flexible for electrical engineering to design the controller of power electronic systems with good performances. [ABSTRACT FROM PUBLISHER]

In this paper a new algorithm is proposed based on multiple feature and particle swarm optimization for target tracking. The multiple feature includes local binary pattern (LBP), phase congruent (PC) and gradient magnitude (GM). This method not only can make use of contrast invariance of phase congruent, but also can fully utilize the rotation invariance of local binary pattern. Compared with the traditional histograms, the proposed algorithm extracts effectively the edge and corner feature in the target region, which characterizes better and more robustly represents the target. The experiments show that the proposed method in this paper is more accurate and more efficient in tracking objective than the traditional algorithms. [ABSTRACT FROM PUBLISHER]

Demand management is mechanism to shift the demand of electricity from peak to off-peak to use the available energy as efficiently as possible without requiring additional generation capacity or transmission and distribution infrastructure. Demand response is a special type of demand management which motivates the customers to respond to electricity prices over time. The recently proposed demand response strategies aim at reducing the energy cost of the whole system by shifting energy consumption from peak to off-peak. These methodologies are complex and resource consuming as they try to reach a global optimum for a large group of residences. The paper proposes a simpler demand response strategy to minimize the energy cost in each residence independently using binary particle swarm optimization (BPSO) that schedules the electricity consumption of the shiftable loads in each residence. The paper also proposes a modification of the BPSO algorithm that assists the scheduling algorithm to converge quickly. [ABSTRACT FROM PUBLISHER]

This paper illustrates how a 3 degrees of freedom, Cartesian robot can be given the task of playing ping-pong against a human player. We present an algorithm based on particle swarm optimization for the robot to calculate when and how to hit an approaching ball. Simulation results are shown to depict the effectiveness of our approach. Although emphasis is placed on sending the ball to a desired point on the ping pong table, it is shown that our method may be adjusted to meet the requirements of a variety of ball hitting strategies. [ABSTRACT FROM PUBLISHER]

Using hybrid renewable energy is one of the best alternatives to supply the electrical energy at remote areas. Renewable energy sources are depended to weather conditions or other factors, so for supplying load with renewable sources appropriate capacity of these sources should be selected. In determining the capacity of renewable energy such as wind and solar, considering the stochastic nature of wind speed and solar radiation is very impressive. In this paper, a new algorithm for determining the capacity of hybrid wind, photovoltaic and battery generation system with considering the uncertainty in wind and photovoltaic power production is proposed. The algorithm of determining capacity of wind, photovoltaic and battery for supplying certain load is formulated as an optimization problem that the objective function is the minimization of the cost and with constrain of having specific reliability. Probability density of wind speed and solar radiation and Mont Carlo simulation method is used to considered uncertainty in wind and photovoltaic power generation. Particle swarm optimization is used for optimal sizing of the system. [ABSTRACT FROM PUBLISHER]

Over last two decades, solar and wind energies have become an alternative to traditional energy sources. These alternative energy sources are non-polluting, free in their availability and are renewable. Another alternative is tidal energy, which is related to falling and rising ocean water levels. Tidal behavior can be predicted easily and this means that if it is enabling to harness tidal energy, it can be produced power for specific periods of time. In this paper, a new hybrid combination of renewable energy sources is considered. The combination of wind, photovoltaic and tidal is taken into account as a primary and battery as an auxiliary source. The aim of design is to minimize the annualized cost of the generation system over its 20 years of operation. Determining the capacity of hybrid wind, photovoltaic, tidal and battery is formulated as an optimization problem. The objective function is the minimization of the cost with the constrain having specific reliability index. To evaluate the system reliability level, the Equivalent Loss Factor (ELF) has been used as an index. Comparison between hybrid wind, solar and battery and new combination of renewable energy is done and it is shown that the combination of wind, solar, tidal and battery is more economical. Particle Swarm Optimization (PSO) is used for optimal sizing the system. Simulation results are carried out by MATLAB software. It is shown that hybrid system is the best configuration that has minimum cost and can satisfy all constrains. [ABSTRACT FROM PUBLISHER]

The role of metaheuristic optimization algorithms in the analysis of real world optimization problems is significantly increasing against traditional optimization methods. But these algorithms possesses the limitation that they are highly problem dependent. The selection of an optimization algorithm for a specific application can be validated using standard test functions. A comparative study of three metaheuristic algorithms, Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm using standard test functions is presented in this paper. Standard test functions which are very much similar to real world optimization problems are used for the performance comparison of optimization algorithms. [ABSTRACT FROM AUTHOR]

The photovoltaic system is the most popular renewable energy to be used due to its environmentally friendly and long term of the economic outlook. However, since its performance depends on the solar radiation and temperature, its maximum output is ever changing. The maximum power point tracking method is commonly used to handle this problem over the years. However, the method tends to get stuck in local minima hence given not optimal result. This paper combines the use of swarm-based intelligence method called particle swarm optimization method and maximum power point tracking to enhance the capability and efficiency of PV system under a variation of temperature and irradiance. The MATLAB program is used to simulate the proposed solution. The simulation results indicate the proposed method produce good result in terms of performance and efficiency in every variation environment factor it is tested. [ABSTRACT FROM AUTHOR]

With the gradual advancement of industrial production, overhead cranes have been widely used in industrial sites such as steel works, ports, warehouses, etc., and underactuated overhead crane system has also become one of the hot topic in the automation field. To make the overhead crane positioning accurately and anti-swing quickly, the paper design a positioning and anti-swing controller based on PSO algorithm optimization PID parameter. Compile PSO algorithm procedures and the invoking procedures which link Matlab and Simulink, combine with crane’s hanging weight system model in Simulink, and realize the PSO-PID control system of the hanging weight simulation. The simulation results show that the trolley can positioning accurately and the swing of hanging weight system can be restrained quickly and effectively [ABSTRACT FROM AUTHOR]

Locating and Sizing of Distributed Generation (DG) is an important research topic in the development of microgrids. First, this paper models the uncertainty of its two main Distributed Generation, wind power generation and photovoltaic power generation. Then, considering the economic benefits and technical requirements, three important indicators including investment benefit, network loss improvement and static voltage stability improvement are selected to optimize locating and sizing of DG, and the particle swarm optimization algorithm with inertia weight is used to solve the problem. Finally, the simulation analysis of the IEEE-37 node test feeder shows that the method can effectively improve the voltage level of the system, reduce the active power loss, and improve the static voltage stability of the system. It has certain practical value in the distribution network planning. [ABSTRACT FROM AUTHOR]

This paper presents an efficient multi-fidelity Bayesian optimization approach for analog circuit synthesis. The proposed method can significantly reduce the overall computational cost by fusing the simple but potentially inaccurate low-fidelity model and a few accurate but expensive high-fidelity data. Gaussian Process (GP) models are employed to model the low- and high-fidelity blac-kbox functions separately. The nonlinear map between the low-fidelity model and high-fidelity model is also modelled as a Gaussian process. A fusing GP model which combines the low- and high-fidelity models can thus be built. An acquisition function based on the fusing GP model is used to balance the exploitation and exploration. The fusing GP model is evolved gradually as new data points are selected sequentially by maximizing the acquisition function. Experimental results show that our proposed method reduces up to 65.5% of the simulation time compared with the state-of-the-art single-fidelity Bayesian optimization method, while exhibiting more stable performance and a more promising practical prospect. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization Based Fuzzy Hyper Rectangular Composite Neural Network (PFHRCNN) combines three computational intelligence approaches including hyper-rectangular composite neural networks (HRCNN), fuzzy systems and Particle Swarm Optimization (PSO). This paper presents a detailed analysis of all PFHRCNNs related studies since the introduction of these studies in the literature up until today. In this context, feature extraction methods that are used in these studies are examined. Then, number of classes, features and samples used in these studies are determined. Finally, recognition success rates of PFHRCNN and other alternative methods are compared. Also, all studies in this area of interest are examined according to the computational results of PFHRCNN and HRCNN for computation time (complexity), recognition rate (success) and number of rule metrics. In the light of the proposed analysis, it can be concluded that the highest success rate is obtained by PFHRCNN approach compared to the other models. However, this approach might be considered as disadvantageous in terms of calculation time. [ABSTRACT FROM AUTHOR]

This paper proposes a novel network anomaly detection method employing wavelet fuzzy neural network (WFNN) to use modified Quantum-Behaved Particle Swarm Optimization (QPSO). Meanwhile, wavelet transform is applied to extract fault characteristics from the anomaly state. Fuzzy theory and neural network are employed to fuzzify the extracted information. Wavelet is then integrated with fuzzy neural network to form the wavelet fuzzy neural network (WFNN). The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. However, the QPSO also has its own shortcomings. So, there exists a modified QPSO which is used to train WFNN in this paper. Experimental result on KDD99 intrusion detection datasets shows that this WFNN using the novel training algorithm has high detection rate while maintaining a low false positive rate. [ABSTRACT FROM AUTHOR]

The paper presents a review of intelligent techniques used for the optimization of an UAV mission plan using sensory input. The constrained problem is evaluated using evolutionary algorithms (such as GAs and PSO), while extenics logic is used to evaluate the sensor fusion state. The review is part of a innovative project dealing with swarm UAV missions and AirWing testing. [ABSTRACT FROM AUTHOR]

This paper investigates how to correct the pass-band droop in a CIC (cascaded-integrator-comb) filter with multiple stages. This filter has been recently proposed in the literature. However, the method to correct the pass-band characteristic is not considered. Instead, the technique utilized for sharpening CIC is applied. The structure requires a minimum number of adders per output sample (APOS) for a given minimum worst-case folding band attenuation and pass-band edge. We consider the designs for narrow-band and wide-band cases. The magnitude characteristic in the wide-band case is chosen as a product of two sinusoidal, while one single sinusoidal function is utilized in the narrow-band case. The design parameters are the amplitudes of the sinusoidal functions in both cases. We propose applying particle swarm optimization (PSO) to find optimal values of design parameters. Next, the optimal parameters are presented in a power-of-two form to get a multiplier-less design. The proposed technique is illustrated with examples. [ABSTRACT FROM AUTHOR]

In this study, the problems and issues that arise in a real-time power system are discussed. An Automatic Voltage Regulator(AVR) maintains the magnitude of the terminal voltage of a synchronous generator at a reference point. Therefore, a proportional-integral-derivative (PID) controller is employed to boost the dynamic response of this AVR in the Power System. The PID controller must be accurately tuned in order to acquire the optimal values of the gainsk_p,k_i,k_dfor the AVR to function properly. For optimizing the PID controller gain parameters, the paper examines optimization approaches such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and also a soft computing technique Fuzzy Logic. A comparison is being done in order to determine which of the strategies is more effective in optimizing specific parameters. Future applications are also being considered. [ABSTRACT FROM AUTHOR]

This work presents an optimal design for a Doubly Fed Induction Generator (DFIG) wind turbine system based on grid-connected back-back converters. The main contribution of this work is introducing a complete design for DFIG and controller system-based wind systems. The DFIG wind turbine's closed-loop vector control system is precisely modelled. The decoupling vector control strategy is used for both rotor and grid-side converters. The mathematical model of the grid side and rotor side with the equation needed for grid synchronizing are described in this paper. The particle swarm optimization algorithm (PSO) is used to adjust the proportional-integral (PI) controller parameter to achieve optimal performance under variable wind speed. A large-scale wind generator system, 2MW, is presented to validate the proposed design. The simulations are run on a grid-connected wind system to demonstrate the system's control performance with the optimal strategy. The simulation results showed the optimal design of DFIG is very suitable for connected grid system-based wind conversion systems. [ABSTRACT FROM AUTHOR]

The research into Photovoltaic (PV) systems under varying conditions led to developing a new maximum power point tracking technique. Commonly used techniques result in lower efficiency when faced with changeable conditions. Therefore, based on the Particle Swarm Optimization (PSO) algorithm, the proposed algorithm improves the output power of photovoltaic (PV) systems under such varying circumstances. The proposed method is tested in MATLAB/Simulink under different states involving non-uniform irradiance and variable temperatures. To examine its performance enough, the outputs of the suggested method which compared with those of conventional techniques. The goal of this paper is to explain the scheme and modeling of a particle swarm optimization algorithm linked with a boost converter for finding the maximum permitted power of solar photovoltaic panels in response to changing of atmospheric conditions. For DC to AC conversion, the CHMLI five levels can be applied to obtain high-quality output power and a low harmonic content for the output sinusoidal wave. The simulations explained that PSO method with Multilevel Inverter (MLI) has a lot more effective tracker capability, is significantly faster, and has a lower settling time and overshoot than conventional techniques. [ABSTRACT FROM AUTHOR]

In this paper, an Automatic Voltage Regulator (AVR) system for nonlinear Synchronous Generators (SG) with a wide variety of working circumstances is designed to keep the SG's terminal voltage stable and consistent. The proposed controller for AVR is an augmented PI controller with a sigmoid function to reduce external disturbances and uncertainty. The augmentation of the sigmoid function is utilized to increase the robustness of the AVR system due to its high nonlinear gain characteristic. Particle swarm optimization (PSO) is used to optimize the gains of the PI controller and augmented PI controller for AVR. The robustness improvement of AVR through the utilization of an augmented PI controller is tested by three different methods, which are: changing generator load, changing generator parameters, and adding external disturbance (noise). The results of the terminal voltage of SG show the proposed augmented controller gives a better-controlled response than that of optimized PI and also gives better robust control through the use of different loads and synchronous generators (generator parameters), in addition to the use of an additive white noise to the input of the generator (output of the controller). [ABSTRACT FROM AUTHOR]

Permanent Magnet DC motors (PMDC) have become important for their many applications that require precise speed control. The motor speed may change suddenly due to external conditions and disturbance of load, so the speed profile will deteriorate and hence the performance will degrade. In this paper, a control design based on conventional and advanced controllers is established for the speed control of a PMDC motor. Firstly, conventional controllers based on proportional integral derivative (PID) were applied. Secondly, advanced controllers using Sliding Mode Control (SMC) are designed. In addition, a Particle Swarm Optimization (PSO) algorithm has been developed to improve the performance of proposed controllers by optimal tuning of their parameters. Third, the controllers were implemented practically in a real-time environment using a PC, an Arduino UNO, and actual PMDC motor and conditioning circuits. The computer simulation showed the superiority of the SMC controller over other controllers in terms of dynamic performance, steady-state and robustness characteristics when the motor is subjected to variation in load. The proposed controllers were validated in real-time to verify the simulation results via experimental setup. [ABSTRACT FROM AUTHOR]

The rising energy demand for the residential sector is a new opportunity for load management to enhance energy consumption, especially during peak load periods. In the smart grid, peak demand periods have a significant impact on energy consumption. During peak periods, due to increasing demand, the utility company must increase its generation capacity. For this reason, the demand response of smart grids has helped to balance the required energy demand with the available generation resources, which lately gained widespread attention. The proposed system addresses the possibility of off-location control of household electrical appliances through the integration with the Internet of things (IoT) Monitoring and controlling energy use in a building is made easier by the use of building-energy monitoring systems. minimize power consumption to the barest minimum. The key feature of the proposed system is the ability to remotely monitor and manage electrical and electronic appliances to minimize peak load to the barest minimum. The plan has both efficiency and accuracy advantages, as well as financial advantages, Particle Swarm Optimization (PSO) is employed to optimize the load consumption of a typical residential building, and Home Energy Management Systems(HEMS) is an intelligent system that performs the functions of planning, monitoring, and controlling energy that is used inside a building. It is intended to offer a desirable demand response based on the system conditions and the price scheme by the utility. A smart plug helps a consumer remotely monitor and control the electrical appliances. Raspberry Pi has been used and programmed to become one of the main elements of the Energy Management Controller (EMC). It can send and receives commands to a smart plug using the Message Queuing Telemetry Transport (MQTT) protocol according to the optimal scheduling time of household appliances, to add to that, show in this paper demonstrates is that how the device may be accessed and controlled to reduce energy use in the home, as well as a 24.31 percent reduction in the cost of energy consumption. based on the optimization method modeling. [ABSTRACT FROM AUTHOR]

In the field of power system, making the system stable from various disturbances is the most significant process. For power system continuity, it is necessary that system should maintain perfect synchronism and the system should be free from oscillations for continuous and efficient output. This paper gives the best solution to solve the low frequency fluctuations which are based on the optimization criteria of a generator and the load angle. Parameter optimization problems such as system eigen values, system damping ratio and time domain error deviations are formulated in the proposed controller design. Based on the open loop results of the system, three controllers are included in the system model for damping of the fluctuations. The proposed naturally inspired Math Flame Optimized controller exhibit good damping performance compared to conventional and Particle swarm optimized controllers. [ABSTRACT FROM AUTHOR]

This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA). And the parameters of support vector machine (SVM) were optimized with genetic algorithm (GA), particle swarm optimization (PSO) and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast. [ABSTRACT FROM AUTHOR]

This paper introduces a novel technique for efficiently combining Particle Swarm Optimization (PSO) with method of moments (MOM) for computing the resonant frequency and bandwidth of rectangular microstrip antenna. In this technique, the problem is formulated in terms of an integral equation which is the kernel of a dyadic Green's function. After this PSO which will be introduce to determinate antenna parameters by optimizing the impedance matrix ∣Z∣. The resonant frequency results obtained by using (PSO/MOM) algorithm are in very good agreement with the experimental results available in the literature. The computation time is greatly reduced as compared to the classical MOM. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Particle Swarm optimization based on constriction factor, inertia weight factor and Cauchy distribution for power energy generation fuel cost optimization for thermal power plants using different type of fuels. The method is also compared to other versions of Particle Swarm Optimization (PSO) including original PSO and PSO based on the combination of constriction and inertia weight factors. A system with ten thermal unit systems using three fuel types is used as the main test case for investigating the performance of the proposed method. Two load levels with 2400 MW and 2500 MW are used to calculate the used power energy generation fuel cost of the three PSO methods. In addition, the proposed method is also compared to previous methods shown in the literature for conclusion of the real performance. As a result, the proposed PSO method outperforms two other PSO method and other previous methods about reaching smaller power energy generation cost and having a stronger search ability. Thus, it concludes that Cauchy distribution is very useful for PSO and it can be used for other optimization problems in power systems. [ABSTRACT FROM AUTHOR]

Roller bearings are used to support the driveshaft of a rotating machinery and are suitable for handling the radial load. In this paper, multi objective design optimization of roller bearing configurations namely Spherical Roller Bearing and Needle Roller Bearing have been carried out. The two optimization objectives are chosen in this study to enhance the working life of the bearings. The objective functions considered are Dynamic Capacity (DC) and Elasto-Hydrodynamic Lubrication Minimum Film Thickness (EHLMFT) of the bearing. These two objectives are conflicting in a real-life scenario and the trade-off between the two objectives has been obtained using the Multi-Objective Optimization approaches. Traditional weighted sum and pareto optimal methods are implemented in this study to obtain optimal design parameters. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms are used to arrive at optimum solutions for the problem formulated using the Weighted Sum Method (WSM). Two nature-inspired meta-heuristic approaches namely NSGA II and Multi-Objective Feasibility Enhanced PSO (MOFEPSO) are used in this study to obtain non-dominated solutions. The results show that MOFEPSO efficiently solves the problems of both Spherical and Needle roller bearing design. [ABSTRACT FROM AUTHOR]

The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method. [ABSTRACT FROM AUTHOR]

This paper speaks to the changed Tropos objective model for handling the danger related with the product advancement cycle. Here the modification is done by the hybrid optimization algorithm. The tropos model will be improved by selecting the optimal candidate solution by using Hybrid Gravitational Search Algorithm – Particle Swarm Optimization algorithm. The proposed approach characterizes a strategy to dissect the relationship between the hubs of each layer to assess their odds of raising the dangers. Here the risks are prioritized and which are identified by the improved Tropos method. The obtained results show that, the proposed optimization-based goal model has delivered better performance in terms of time and memory requirement when compared with the normal model and also the risk and cost of the proposed model is proved to be more efficient than existing model. [ABSTRACT FROM AUTHOR]

This paper presents an experimental verification of selective harmonic elimination for three-phase cascaded multilevel inverters. The Particle Swarm Optimization (PSO) algorithm is used to compute the non-linear equation for the various modulation indexes of a seven-level three-phase inverter. In addition, the appropriate switching angles are effectively calculated by using the PSO algorithm. Non-triplen harmonics up to 7th order are eliminated. To verify the computational results, the three-phase cascaded multilevel Field Programmable Gate Array (FPGA) based is developed. The results show that the developed multilevel inverter with selective harmonic elimination pulse width modulation switching angles manage to eliminate the lower order harmonics, which is not only for nominal frequency (50 Hz) but also for different operating frequency. [ABSTRACT FROM AUTHOR]

The work in this paper is focused on special assessment and evaluation of mooring system of ship-shaped FPSO unit. In particular, the purpose of the study is to find the impact on mooring loads for the variation in different parameters using MIMOSA software. First, a selected base case was designed for an intact mooring system in a typical ultimate limit state (ULS) condition, and then the sensitivity to mooring loads on parameters e.g. location of the turret, analysis method (quasi-static vs. dynamic analysis), low-frequency damping level in the surge, pretension and drag coefficients on chain and steel wire has been performed. It is found that mooring loads change due to the change of these parameters. Especially, pretension has a large impact on the maximum tension of mooring lines and low-frequency damping can change surge offset significantly. [ABSTRACT FROM AUTHOR]

In view of the needs of the vehicle unit, this paper has carried on the research to the sensor distribution question. In the case of fully taking into account the information such as target identity and target priority, the mathematical model of optimal sensor allocation is established based on the maximum information gain criterion, and then the population-based incremental learning algorithm is used to solve. Finally, the validity of the method is verified by solving the problem of sensor assignment problem of the vehicle unit. [ABSTRACT FROM AUTHOR]

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems. [ABSTRACT FROM AUTHOR]