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In this paper we present an application of two Computational Intelligence Algorithms, namely Particle Swarm Optimization (PSO) Algorithm and Differential Evolution (DE) for finding and optimal solution to two optimization problems that occur in a paper industry. The first problem deals with the economic optimization of a hypothetical but realistic Kraft pulping process and in the second problem we have considered the optimization of Boiler load allocation problem. Both the problems form an integral part of paper making process. The simulation results show the efficiency and time effectiveness of DE and PSO. [ABSTRACT FROM AUTHOR]

To deal with non-paper test composition algorithm impact on exam quality, we proposed the test-sheet composition algorithms. By comparing a variety of existing intelligent algorithms in the application of test-sheet composition, we identify the shortcomings of existing algorithms, such as the "premature" of algorithm due to the poor local search ability and the low convergence rate, etc. PSO algorithm has no crossover, mutation operators. It directly provides the speed, position update formula, and completes the assessment with the help of the fitness function of iterations. The principles and mechanisms of algorithm are simpler. On the basis of standard PSO algorithm, we proposed a Binary Particle Swarm Optimize (BPSO) algorithm based on probability. Bayes formula was used to overcome the human factors impacting on algorithm convergence speed. The algorithm validity has been shown in the simulation experiment with Java. [ABSTRACT FROM PUBLISHER]

This paper discusses on the applications of non-traditional method. There is different non-conventional optimization are reviewed to solve the optimization problems. In this survey, the methods we are going to review are Krill Herd Algorithm (KHA), Pattern Search Algorithm (PSA), Sal Swarm Algorithm (SSA) and Gradient Based Algorithm (GBA). These methods are approach to find the optimization methods can solve the linear and non-linear optimization problems and results the global values. These methods are broadly reviewed. [ABSTRACT FROM AUTHOR]

The residential load sector ensures power system stability and effective energy management. Despite efforts to integrate renewable energy sources and develop information and communication technology, the residential sector's flexibility, energy management, and scheduling challenges persist, impeding the power grid's stability and efficiency. Demand Side Management (DSM) has emerged as a crucial solution to address these challenges. This paper proposes a DSM model that employs Binary Particle Swarm Optimization to perform residential load scheduling while considering a time-of-use pricing scheme. Simulation results demonstrate a significant reduction in energy costs, validating the proposed model's effectiveness in enhancing the power grid's stability and efficiency. By adopting this approach, residential consumers can efficiently manage their energy consumption, reducing overall energy costs while contributing to the power grid's stability and efficiency. The proposed DSM model represents a significant step towards achieving a sustainable and cost-effective energy future, offering a compelling argument for its adoption in residential settings. This paper contributes to the field of energy management by providing a technically rigorous and persuasive analysis of the benefits of DSM in residential load scheduling. [ABSTRACT FROM AUTHOR]

In the present era heart disease is considered to be one of the major diseases and many people are suffering due to this disease and the foremost challenge is identification and prediction before it causes any consequences or deaths. There are some techniques available for prognosticate heart disease since this disease is increasing rapidly throughout the universe, this prediction process may save life. Time and efficient play important role in identifying heart disease in healthcare industry particularly in the field of cardiology. In this paper we developed a dynamic and accurate system for heart disease prediction using machine learning techniques. There are two phases which can identify and predict heart disease: 1) Feature selection 2) classification stage. Feature selection is one of the methods for selecting attributes and feature subset as it eliminates unwanted data and apply classification algorithms and dataset comprises of patient's attributes like age, gender, blood pressure, glucose level, blood sugar etc... by processing these attributes we can predict the chance of occurring heart disease. This paper proposes a optimization techniques like Grey wolf optimization, Particle swarm optimization combined with Ant colony optimization for performing supervised classification algorithms.PSO is for finding optimum solutions and ACO is for finding good paths and the mixed proposed algorithm is applied and result are estimated to identify the efficiency and robustness. [ABSTRACT FROM AUTHOR]

A shaft is used to transmit power from a device that produces power to a gadget that absorbs energy. The strength of the hollow shaft is greater than the equal weight stable shaft. Hollow shafts have a greater polar second of inertia, which permits them to transmit greater torque than stable shafts. Non-Traditional Optimization strategies are efficaciously used to resolve many engineering problems. This paper uses ten non-traditional optimization techniques to discover the weight minimization of a hollow shaft. The comparative outcomes display that the Particle Swarm Optimization outperforms different techniques. [ABSTRACT FROM AUTHOR]

This dissertation concentrates on the application of non-traditional optimization techniques. To solve the optimization problem, the various non-traditional optimization methods are reviewed critically. The methods that are going to review in this survey are Particle Swarm optimization (PSO), Crow Search Algorithm (CSA), Charged system search algorithm (CSSA) and Big-bang big crunch optimization (B-BBCO). These methods are applied to investigate the optimum values. The both linear and non-linear optimization problems are solved to get global solutions. [ABSTRACT FROM AUTHOR]

Water is essential for all human activities, and that rainfall is one of the critical sources of this precious commodity. Prediction of how much rainfall and when it is most likely to occur will assist concerned officials in planning for its storage and subsequent distribution. Meteorological agencies predict rainfall using statistical or dynamic models. Because of the complexity involved in rainfall prediction and limitations of existing techniques, prediction skill improvement is necessary. Recently, researchers in prediction are using intelligent systems such as Artificial Neural Networks, Fuzzy Inference Systems, Support Vector Machines, and Genetic Algorithms. Many network parameters are required to be selected for the use of these systems, and the choice of the parameters affects the accuracy of the model. Experimental discovery of the parameters is one way, and the other way is to use optimization algorithms. In this paper, various optimization techniques used in computationally intelligent systems are surveyed for rainfall prediction. The optimization techniques mainly used for this purpose are Particle Swarm Optimization, Genetic Algorithm, and Ant Colony Optimization. In all the research articles case study of a certain geographical area for rainfall prediction with a different set of inputs and different forecasting lead times is presented, and hence comparison between the models is difficult. For rainfall prediction, model input selection is equally important to the selection of model parameters as a set of predictors change with increasing or decreasing geographical area and forecast lead time. This paper attempts to identify optimization techniques suitable for medium-range rainfall forecasting over a small geographical area. [ABSTRACT FROM AUTHOR]

A new swarm optimization algorithm known as the sparrow search algorithm (SSA) consider in this work. It fascinated a lot of consideration due to its extremely good attributes. However, it has some pitfalls like minimum global search ability, falling into local optima etc which were found by many researchers. In this paper,first time sparrow search algorithm (SSA) is tested on welded beam design (WBD) optimization problem. Welded beam design (WBD) optimization problem is an engineering optimization problem that has been solved by various metaheuristic algorithms such as SiC-PSO, neuromorphic optimization (NSO) technique etc. Obtained results of SSA were compared with othertechniques such as GWO, PSO, TLBO and it is found that although it works very well on WBD problem with regards to convergence speed, in relation to optimality, convergence accuracy and stability SSA gave the results which were obtained from other algorithms. So, SSA needs to be modified for future work. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) has demonstrated itself to be the most dependable, reliable solar energy source. The output power from solar cells is influenced by the temperature and solar radiation throughout the day, affecting the P-V curve characteristics. A maximum power tracking (MPPT) was achieved to extract maximum power from the PV panel. A comparative analysis between two traditional techniques, P&O and INC and a proposed PSO optimization technique is presented in this paper, based on MPPT efficiency, respond time, oscillation, and its capability to hunt maximum power point (MPP) under changes in irradiance condition and fixed constant temperature at 25°C. These different MPPT algorithms were implemented in MATLAB / Simulink to control the duty cycle of the dc-dc boost converter. Particle swarm optimization has been proven to be more effective than other methods. [ABSTRACT FROM AUTHOR]

In the deregulated environment, the transmission grids are used optimally. This utilization of the transmission system makes some lines congested due to the capacity constraints of the line. Congestion becomes a barrier of power trading and it affects the security of the power system. Congestion Management (CM) acts as a major issue that threatens the system security and it is a most difficult task for the system operators. This paper tries to introduce a novel optimization based CM model with advanced soft computing technique. An algorithm is introduced in this paper to deal with CM, which obviously optimize the generating power of added generators with the bus system. This manages the congestion with minimum rescheduling cost. The proposed optimization algorithm termed as Whale Optimization algorithm (WOA) involves in the management of congestion optimally. Subsequently, the experimentation is performed in the test bus system of 118 bus systems. The effectiveness of proposed model is compared with the conventional methods, with respect to cost and convergence. [ABSTRACT FROM AUTHOR]

In this modern world, human life has become increasingly reliant on electricity, which serves to be one of the basic needs of human to lead a normal life. As the usage of electricity has increased over the years, the consumers are very much concerned about its consumption rate and the electricity bill generated out of it. Hence monitoring the consumption rate stands first which is very trivial and challenging by designing an efficient energy management system. This paper outlines the survey on energy management systems implemented with artificial intelligence techniques towards finding a viable solution to the aforementioned issue. This study elucidates how artificial intelligent systems are incorporated in different energy management systems to match demand and supply. A comparative analysis of different intelligent techniques with optimization goals, issues and solutions, applied to the energy management systems for effective functioning is also presented. Finally, Home Energy Management System (HEMS) using Binary Particle Swarm Optimization Algorithm (BPSO) is presented. 26% reduction in the daily bill with optimization of HVAC and non-interruptible appliances was attained. Due to the interrupted supply of energy sources, effective storage model is determined to be an alternate viable option owing to technological advancement and capacity of ensuring excellent grid services. Future directions in terms of developing hybrid systems using hybrid energy sources and intelligent systems are also suggested. [ABSTRACT FROM AUTHOR]

Recently assembly line balancing (ALB) is an ultimate focus issue in manufacturing industries for optimizing both common goals which were the number of workstations and the production rate. Metaheuristic is one of optimization problem-independent technique. This paper focusses on the short review of metaheuristic optimization in solving assembly line balancing problems. Hence, to produce a summary of the review, this paper selects some publications for the past 3 years from year 2016 to 2019. From the review shows the metaheuristic optimization that often used are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Differential Evaluation (DE), Evolutionary Algorithm (EA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Besides, GA is most frequently used to solve ALB problems. Finally, recommendation for future research for solving ALB Problems using metaheuristic optimization are discussed. [ABSTRACT FROM AUTHOR]

Maximum Power Point Tracking (MPPT) can be defined as the most efficient way to get the most power out of a photovoltaic device. Under any environmental scenario, the fuzzy logic controller algorithm will excellently monitor the full power operating point. The implementation of the fuzzy logic algorithm for the MPPT charge controller is defined in this paper this paper describes how to use a Particle swarm optimization - fuzzy logic algorithm to incorporate an MPPT charge controller to get the most peak power out of a photovoltaic device. The boost converter uses the Fuzzy Algorithm to transform the PV panel voltage to a fixed level where the PV panel's full peak power can be collected. The high rating current is converted into a particular voltage spectrum by a DC/DC converter. The PV panel's peak DC power is then used to charge the DC battery. Simulink is used to demonstrate the simulation result (MATLAB). [ABSTRACT FROM AUTHOR]

At present, the fault location technology of distribution network mainly relies on FTU, which is divided into direct method and indirect method. The direct method is mainly matrix method, and the indirect method is mainly based on AI algorithm. The matrix method can not locate the fault correctly when the FTU is disturbed and other reasons cause the failure information to be missed. So there are some problems in fault location of distribution network using matrix method (direct method). To solve this problem, based on the traditional particle swarm optimization (PSO) algorithm, this paper constructs a binary particle swarm optimization (PSO) algorithm model to realize fault location of distribution network, and uses MATLAB to verify the simulation example. The results show that the algorithm has the advantages of good convergence, high stability and excellent fault tolerance. [ABSTRACT FROM AUTHOR]

There has been a lot of research in recent years into employing optimization approaches to improve artificial intelligence (AI). In this research review paper, we have compared and contrast some of the most usual optimization algorithms, such as Backtracking searching method (BSA), the genetic algorithm (GA), particle swarm optimization (PSO), an artificial bee colony (ABC), and the genetic algorithm, which are all artificial neural networks (ANNs)-based algorithms. The number of recently developed optimization techniques, such as the lightning search algorithm (LSA) and the whale optimization algorithm (WOA) are also compared. All the techniques are categorized based on randomly generated populations. To produce the best possible results, the processing parameters are set within a certain range according to their knowledge. This review paper emphasis on applying optimization techniques to improve the accuracy of simply adjusting the parameters of the neural network. This review paper also presents some results for enhancing neural network performance using various optimization techniques like PSO, GA, and ABC optimization methods to get optimal processing parameters, such as the number of hidden layers, neurons and learning rate etc. The findings of this research review paper will aid in the improvement in the quality of plastic injection molded parts. [ABSTRACT FROM AUTHOR]

This paper proposes a new Pulse Width Modulation technique for single phase cascaded H-bridge multilevel inverter for maintaining the output voltage across the load side with reduction in Total Harmonic Distortion (THD). The novelty of this paper is to equalize the area under the multilevel output voltage with that of area under the pure sine wave in discrete time periods. The main objective of this design is to maintain the desired output voltage with minimization of THD at the dynamic load conditions. The comparison between the proposed PWM technique with conventional Optimized Harmonic Stepped Waveform PWM is also shown and the results are proven that the proposed method is comparatively better. Particle Swarm Optimization algorithm is employed for solving the non-linear objective function and finding out the optimal switching angles for the MLM switches. MATLAB software is used to simulate the proposed design. The detailed mathematical modeling on the area equalization techniques with the advantage of using the proposed method than OHSW PWM technique is also presented in this paper. [ABSTRACT FROM AUTHOR]

The study here summarizes the work growth in "Particle Swarm Optimization (PSO)" since (1995 to 2021). Thirty-five papers have been analysed. Those research articles have been divided into various aspects in which PSO technique has been modified a lot during these years due to its simplicity. The hybridization of PSO with many evolutionary algorithms has been done during these years which helped us a lot to solve many optimizations and real-life problems. This paper discusses the progress, modification, improvement and application of PSO during these years. [ABSTRACT FROM AUTHOR]

One of the most gravitate issues in the world is energy usage. As the largest consumer in terms of energy usage, the manufacturing sector had introduced a lot of approaches to decrease their energy utilization. As the energy usage issue becomes more prominent, researchers had applied the Assembly Line Balancing (ALB) optimization that considers energy utilization as one of their efforts in reducing energy consumption. This paper discusses the related papers done by researchers, demonstrates the ALB model developed with electrical energy consideration by the application of the Matlab simulation and its validation through the manual hand calculation. The ALB with Energy Consideration (ALB-EC) was modelled into a mathematical model that could be used in solving the Simple Assembly Line Balancing Problem (SALBP), with the application of the Matlab application. The Particle Swarm Optimization (PSO) algorithm was applied and the model was tested by using three problems which consist of each of a small, medium, and large-sized test problem. Based on the finding, we could achieve the same results on the computational method along with the manual hand calculation for the evaluated total energy and objective functions. Besides, the advantages and limitations of the proposed model were also discussed in this paper. Through the application of this study, it could contribute to the reduction of electrical energy usage in the production line, which can reduce the overall energy usage CO2 gas production and prevent climate change. [ABSTRACT FROM AUTHOR]

Dc motors represent linear systems up to point of saturation. In this paper, optimized tuning of DC motors has been discussed with the help of different meta-heuristic algorithms. The model of the DC motor is basically a third-order system. Dc motors, that are used in different industrial applications including conveyors, turntables, and other places where adjustable speed and constant or low-speed torques are required, owing to their simple configuration. They also find its application in dynamic braking and reversing applications as well. Here, in this paper Genetic Algorithm, Differential Evolution, Teaching Learning Based Optimization, Particle Swarm Optimization with different performance indices (Mean Square Error and Integral time absolute error) is compared with the standard Ziegler & Nichols method. Comparison of results using standard step parameters i.e., maximum overshoot, steady-state, rise time and peak time, etc. is being discussed. [ABSTRACT FROM AUTHOR]

Smart growth focuses on building sustainable cities, using compact development to prevent urban sprawl. This paper establishes a series of models to implement smart growth theories into city design. Besides two specific city design cases are shown. Firstly, We establishes Smart Growth Measure Model to measure the success of smart growth of a city. And we use Full Permutation Polygon Synthetic Indicator Method to calculate the Comprehensive Indicator (CI) which is used to measure the success of smart growth. Secondly, this paper uses the principle of smart growth to develop a new growth plan for two cities. We establish an optimization model to maximum CI value. The Particle Swarm Optimization (PSO) algorithm is used to solve the model. Combined with the calculation results and the specific circumstances of cities, we make their the smart growth plan respectively. [ABSTRACT FROM AUTHOR]

IoT and its effect in sharing information has been a huge mutation for Internet; increasing development and use of internet in healthcare departments requires more optimized performance in the speed of sharing and publishing information and energy consumption. An important aspect in this field is routing optimization in IoT; we introduce a new idea, which is a combination of two algorithms: PSO optimization algorithm (particle swarm optimization) and GA algorithm (genetic algorithm) and it is used for routing optimization and latency reduction during sending information between IoT groups in an IoT healthcare system. [ABSTRACT FROM AUTHOR]

This paper displays a hardware prototype in order to correct the power factor of Brushless Direct Current motor drive by making use of the Cuk converter with Particle Swarm Optimization controller. For less power and very low cost application this drive can be used. The speed of Brushless Direct Current motor was controlled by differing the DC bus voltage in Voltage Source Inverter (VSI) for least switching losses. The Cuk converter run with both continuous conduction method and discontinuous conduction method even so the diode bridge rectifier was at the back of it for varying the direct current link voltage. This enhance the power factor at AC mains of the system circuit. The PSO controller was used for getting the several best values of measurements by compare and contrast process. The system was simulated by using MATLAB/Simulink software and the hardware prototype model developed gives better performance for various values of speed accompanied with good power factor at AC mains of the system circuit. [ABSTRACT FROM AUTHOR]

This paper describes the buffer optimisation strategy adopted for designing efficient interconnects at 14nm and 7nm technology regime. Investigations are carried out at different wire lengths; ranging from 500 µm to 2000 µm for Cu as well as CNT interconnects. The particle swarm optimization (PSO) methodology is adopted for computing optimus repeater count and size. The results obtained are then validated by comparing it with analytically computed results. The result analysis points out that delay-minimal buffer design strategy may sometimes results in excessive insertion of repeaters, henceforth causing significant power consumption. Thus figure of merit is calculated to investigate the tradeoff between delay and power for Cu and CNT interconnects. From the results it was observed that for carbon nanotube interconnects the quantity of buffers needed is much less as compared to Cu interconnects. For both types of interconnects it is investigated that mere advancement in technology node results in significant increase of buffers to be inserted as repeater. [ABSTRACT FROM AUTHOR]

The concept of forecasting with fuzzy time series have been increasingly developed to solve various problems. The length of the interval and defuzzification are two important factors that affect the accuracy of forecasting results. In this paper, the author combines the fuzzy time series-Markov chain with particle swarm optimization algorithm. Markov chain rule was implemented in the defuzzification for handling the repeated fuzzy sets and determining the proper weights. Meanwhile, the particle swarm optimization was applied for determining the appropriate length of intervals of the fuzzy time series by considering the universe of discourse as a search space and intervals as particles. The author uses the Average Forecasting Error Rate (AFER) value to see the level of accuracy of forecasting. The proposed method was applied to predict the opening stock price of PT. Astra International Tbk. and showed a very good accuracy with an AFER value of 0.9555%. [ABSTRACT FROM AUTHOR]

The convoluted and dynamic nature of cancer growth makes it difficult to predict long-term cancer survival. Evolutionary deep learning techniques have been growing in popularity as efficient and accurate prediction techniques in recent years. Long-term cancer survival prediction has been developed to utilize evolutionary deep-learning techniques. This research combines the power of evolutionary algorithms using Particle Swarm Optimization (PSO) with some deep learning techniques to improve the performance and accuracy of the deep learning models by optimizing its parameters and minimizing the error between the predicted and actual outputs. Overall, combining evolutionary algorithms with deep learning models for cancer survival analysis prediction demonstrates the potential to significantly improve the accuracy and efficiency of cancer survival predictions, leading to better treatment decisions and improved patient outcomes. This paper uses the gbsg2 survival analysis dataset, a well-known dataset commonly used in survival analysis studies. [ABSTRACT FROM AUTHOR]

In this paper, the sparrow search optimisation algorithm (SSA) is utilised to obtain the optimal values for proportional-integral-derivative (PID) controller parameters to reduce frequency deviation and change in tie-line power in interconnected power systems within a deregulated environment. A load frequency control (LFC) was designed and analysed for the proposed power system model, which consists of a two-area reheat steam turbine generator integrated with a solar thermal renewable energy source in area 1 and a geothermal energy plant in area 2. All possible contracts in the deregulated power market, including Poolco, Bilateral, and Contract violations, were thus investigated, and the performance of the proposed controller analysed. The simulation of the proposed model was implemented using MATLAB/SIMULINK, and the robustness of the proposed control technique using a PID controller using SSA compared with the results of using a Particle swarm optimisation technique (PSO) and Gray wolf optimisation (GWO). The SSA algorithm optimised PID controller performance was found to be considerably better in terms of the settling time of power system frequency deviation, with improvements of 71.25%, 67.44%, and 56.39% in terms of Poolco, Bilateral, and Contract scenarios seen, respectively. Moreover, the rise time and steady-state error of the frequency deviation and the change in power in the tie line were also significantly enhanced. The suggested controller was thus shown to be more reliable and to produce superior outcomes in all possible scenarios. [ABSTRACT FROM AUTHOR]

Blood sugar levels rise as a result of impaired glucose homeostasis, which is a feature of Diabetes Mellitus (DM). Coronary artery disease, nerve damage, diabetic retinal disease, kidney failure, and sex disorders are just a few of the consequences that can result from DM. DM has to be diagnosed early to minimize its effects and those of its consequences. One technique for automatically detecting DM based on the prior data is the clustering algorithm. However, the K-mean Clustering method is easy to be implemented, has fast computational time and adapted easily. However, the centroids of K-means are initialized randomly that causes to be stuck in local optima. For this reason, the robust centroids initialization on the K-means clustering method are necessary to handle this problem. This paper uses swarm intelligence algorithm for this goal since it is able to obtain solution near the global optima, is robust and easy to be implemented. As a result, this study proposes an improved K-means clustering-based technique for diagnosing DM. The K-means is improved by using robust centroid initialization-based swarm intelligence algorithm. The swarm intelligence algorithm which is used is Particle Swarm Optimization. By using experiments, the proposed method is confirmed better than the original K-means clustering in diagnosing DM. [ABSTRACT FROM AUTHOR]

The performance of PV systems in fractional or complicated fractional shading situations is impacted by erratic irradiance, temperature, and unpredictable weather patterns. In these conditions, maximizing the O\P of PV schemes becomes challenging. This work investigates the tracking mechanism in HOM under fractional shade environments and complex partial shade conditions to obtain maximum power (MPP) in PV system, and capture global peak (GP) instead of local peak (LP) to make the system Photoelectric stable and high efficiency. Also studying the results obtained from the newly proposed (HOA) and comparing them with smart and traditional swarm optimization techniques such as p&o, improved cuckoo search (ACS), particle swarm optimization (PSO) and dragonfly algorithm (DA). For gradient optimization with regard to PV panels, MPPT use mathematical models Comparison shows HOA outperforms other technologies and achieves the best results in terms of fast tracking and effectiveness, for stability under variable weather factors and the ability to get maximum power. This paper compares the effectiveness of a number of MPPT performances over a broad variety of shading speeds using MATLAB, and proposes a PV array configuration for various systems under various partial shading advances. [ABSTRACT FROM AUTHOR]

The growing demand for electricity and the distance between the power plants and the loads pose a threat to voltage stability and the equilibrium between supply and demand. The system also experiences significant losses in distribution networks and extreme strain on transmission lines. The reliance of the generation on fossil fuels, on the other hand, poses a future issue in terms of shortage as well as the rise in environmental contaminants. The idea of a microgrid is one of the creative ways to combine dispersed energy sources and the utility grid and manage them as a single system. Microgrid may boost system dependability, improve the distribution network, and lessen the strain on the transmission lines. Distributed generator (DG) is put directly in the load center distribution network or at the distribution network. The voltage profile of the system will be improved and overall power losses can be decreased with the help of optimal DG allocation. This paper presents a method for optimally siting and sizing distributed generation (DG) units in a microgrid by binary particle swarm optimization. The DOLPHIN optimization algorithm (DOA) utilizes under voltage load shedding (UVLS). The Direct Backward Forward Sweep Method (DBFSM) depicts the practical load flow technique suited in radial distribution system (RDS), as it's utilized to display the voltage profile and total losses of each node with and without DG and load shed. Four load models were used (normal, constant current, constant impedance, and ZIP model) with comparisons between them. The RDS of the typical IEEE-33 bus is simulated using MATLAB programming. [ABSTRACT FROM AUTHOR]

Alzheimer's is a deadly disease it can cause dementia in sufferers. It is necessary for early detection in the treatment of this disease. Many studies have discussed Alzheimer's disease with data mining techniques, but the most accurate method is unknown. This paper proposed a Naive Bayes algorithm with Particle Swarm Optimization (PSO) selection feature and bagging for optimize unbalanced data. The results of the experiment with 10-fold cross validation, the first test using naive bayes algorithm obtained an accuracy value of 93.75%, with a AUC value of 0.966. Furthermore, the test used with PSO feature selection and bagging technique, and the accuracy value obtained by 98.21% with a AUC value of 0.989. The results of this test can be concluded that the testing of PSO feature selection and bagging techniques, the accuracy value obtained has increased significantly, this proves that the optimization of algorithms with PSO feature selection and bagging techniques has excellent classification. [ABSTRACT FROM AUTHOR]

In a deregulated electricity market, it may always not be possible to dispatch all of the contracted power transactions due to congestion of the transmission corridors. System operators try to manage congestion, which otherwise increases the cost of the electricity and also threatens the system security and stability. In this paper, congestion management by rescheduling of generators has been proposed in pool model without bilateral contracts and pool model with one bilateral contract, also compares the amount of rescheduling in both cases. For optimal selection of participating generators a technique based on generator sensitivities to the power flow of congested lines is used. Also this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and IEEE 118-bus systems. [ABSTRACT FROM PUBLISHER]

Modern artificial neural network architectures and training algorithms are able to achieve high accuracy in a wide range of problems. However, training multilayer neural networks using backpropagation or evolutionary algorithms might take a large amount of time. Extreme learning machines (ELMs) are aimed to resolve this problem by excluding training from the neural network model creation process by randomly initializing weights between input and hidden layers and computing weights between hidden and output layers. However, random weights initialization might lead to suboptimal results produced by the network. In this paper, we apply biology-inspired algorithms, including genetic algorithm with tournament selection, particle swarm optimization, and chaotic fish school search with exponential step decay, to the selection of input weights in ELM, the obtained ELM configurations are applied to solve regression problems. The results of the study show, that population-based algorithms can improve ELM accuracy. [ABSTRACT FROM AUTHOR]

Forecasting the bank profile shape of stable hydraulic channels using empirical, experimental and numerical models is of special consideration among fluid mechanic and river science engineers. In the present paper, the application of soft computing methods is evaluated in predicting the geometry of stable channel cross sections. In this way, using a combination of the Particle Swarm Optimization (PSO) algorithm with an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, a novel evolutionary system called ANFIS-PSO is presented. The evolutionary model performance is assessed in comparison with a simple ANFIS model. The coordinates of points located on a channel boundary in stable state were also measured by the authors using a sensor instrument in the laboratory at 4 different flow discharge rates of 1.157, 2.18, 2.57 and 6.2 l/s. The results indicate that the evolutionary ANFIS-PSO model with Root Mean Squared Error (RMSE) and Mean Absolute Relative Error (MARE) of 0.0132 and 0.1326 performed better than the ANFIS model with 0.026 and 0.1426 error values respectively (almost 97% and 10% decrease in RMSE and MARE value for the ANFIS-PSO model, respectively). This demonstrates the high ANFIS-PSO model accuracy in predicting bank profile characteristics. The robust evolutionary ANFIS-PSO proposed can be used in designing and estimating stable channel dimensions. The second-degree polynomial equation proposed by the evolutionary ANFIS-PSO model can be utilized in predicting the coordinates of other points located on a stable boundary of a channel cross section. [ABSTRACT FROM AUTHOR]

Some doctors face difficulty in diagnosing certain types of skin diseases due to the high similarity among them. Six skin diseases are an example of such types:(seborrheic dermatitis,psoriasis, lichen planus, chronic dermatitis,pityriasisrosea,pityriasisrubra) and collectively called Erythemato-Squamous Diseases. Automated systems for classifying skin diseases have emerged as a result of the phenomenal advancement of computer technology in all areas of life. Automated, effective, and accurate classification of skin diseases is very important for biomedical analysis. Sine Cosine Algorithm (SCA) is one of the modernmetaheuristic algorithms proposed to solve many optimization problems. This research paper offers a new Feature Selection (FS) approach that proposes converting the original SCA to a Binary version (BSCA) for applying in the classification domain to determine the best feature subset based on the wrapper model. Thereafter, Enhancing BSCA by the mutation operator to produce a hybrid approach (ESCA). The mutation is entered as an internal mechanism to preserve diversity and strengthening the SCA's exploration capabilities. The result obtained from the proposed approach ESCA was compared with the BSCA and other FS approaches such as Antlion Optimization Algorithm (ALO), and Particle Swarm Optimization (PSO). The dataset for testing was obtained from the UCI Repository site, it consists of 366 samples with 34 features. The experimental results demonstrate the effectiveness of the suggested approach in extracting optimum features from among the overall features of the dataset. ESCA gave excellent diagnostic accuracy (0.981410) with the ratio of selected features (0.564706). [ABSTRACT FROM AUTHOR]

Since the marine source is vast, an optimized energy converter design is needed to extract as much marine energy as possible. This paper briefly overviewed the optimization technique of hydrofoil for the ocean current energy development. Optimization was considered an essential solution to solve the hydrodynamics design problem formally and systematically. Optimization methods such as Genetic algorithms, Particle swarm optimization, and Computational fluid dynamic were the widely used techniques in achieving the optimal hydrofoil shape for ocean current energy applications. This paper also overviewed the Artificial Neural Network as an objective function for the optimization algorithm to reduce the optimization process time-consuming. It is crucial to improve the optimization process. Based on the outcome of this review paper, the selection of design variables has a significant impact on the optimization process. Therefore, it is recommended to apply different hydrofoil types and adjust the thickness and camber of the hydrofoil where both are essential geometric parameters. Optimization of hydrofoil's shape is crucial to increase the hydrodynamic performance of the turbine blades. Finally, the optimal design of the current energy converter hopefully will help ensure the continuity of energy harvest from the untouchable ocean and reduce the diversification burden on existing technologies yet can also act as a backbone for driving the economy. [ABSTRACT FROM AUTHOR]

The development of internet technology can make it easier for us to communicate with each other. However, this ease also has negative impacts such as illegal and piracy dissemination of digital data. This paper proposes a watermarking scheme based on Discrete Wavelet Transform (DWT), which can protect the ownership and originality of audio data. The R coefficient on QR Decomposition (QRD) is used as a place for watermark data. Afterward, the watermark image is embedded using Quantization Index Modulation (QIM). After all watermarking processes are complete, optimization is carried out for each audio type with the poor BER using Particle Swarm Optimization (PSO). The host tests are guitar, drum, piano, bass, and vocal with.wav format. The experimental result indicates that the performance increases at 67.64 % after applied PSO. Audio watermarking schemes became resistant to various attacks, i.e., compression, filtering, addition noise, and resampling. [ABSTRACT FROM AUTHOR]

An optimization model is proposed to manage a day-ahead optimal energy management strategy for economic operation of Microgrids. The model is based on a using particle swarm optimization algorithm (PSO) for scheduling four energy sources (grid, PV system, wind system, energy storage system) with 24 hours' time step, considering forecasted electrical demands, weather, and renewable energy generations. In this paper, the objective function is to minimize the cost of electricity generation and to manage delivering power from hybrid sources to the demand. The results showed that scheduling and controlling of different energy sources in efficient way reduce the total cost of power generation and ensure sustainable power flow. It is important to enhance the usage of solar and wind sources, optimize the operation of storage systems. [ABSTRACT FROM AUTHOR]

Optimization procedures offer a possibility for time-effective determination of input parameters values for complex soil constitutive models. The following paper presents a combination of the metaheuristic Particle swarm optimization method (PSO) and commercially available solver based on the finite element method (FEM). After the brief theoretical description, different alternatives to the PSO method are reviewed and tested. An optimal alternative is chosen and further used. In the second part of the paper, the combination PSO - FEM is utilized for a fully automatic derivation of input parameters values for the Hardening small strain model from pressuremeter tests. Predicted pressurevolume curves from the axisymmetric FE model gradually converge towards the measured curve until the accuracy criterion is reached. [ABSTRACT FROM AUTHOR]

Optimization plays a key role in various fields of science specially engineering to simplify the decision making process. Long-term production scheduling (LTPS) of open pit mines is a large-scale optimization problem to determine the block extraction sequence to maximize the net present value considering technical, economic constraints. For many optimization problems, finding the best solution is intricate and timeconsuming. In such cases, a combination of mathematical methods and meta-heuristic techniques provides a good solution in a reasonable time. This paper presents a hybrid model between lagrangian relaxation (LR) and particle swarm algorithm (PSO) to solve the LTPS problem under the condition of grade uncertainty. We suggest to apply the LR method on the LTPS problem which to improve its performance speeding up the convergence and also, PSO is used to update the lagrangian multipliers. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. The results of the case study demonstrate the LR approach in solving large-scale problem and produce an acceptable solution is more effective than traditional linearization method. In addition, the suggested hybrid strategy based on PSO, showed better performance than existing methods. The results obtained show that the extended version has brought substantial improvements compared with the first approach. In fact, the results are too close to the best results obtained in the literature. This is due essentially to the diversification added. [ABSTRACT FROM AUTHOR]

Composite tubular structures are increasingly being used in applications with thermomechanical loading conditions such as pressure vessels, rocket nozzles, gas piping and gun barrels. While significant weight reduction over existing monolithic materials is able to be achieved, notable challenges exist in lightweighting optimisation of their layup due to the complexity of the solution space. This paper aims to investigate the use of Particle Swarm Optimisation (PSO) for the layup optimisation of composite tubular structures under combined thermal and mechanical loading. Closed-form 3D elastic solutions are used to determine the stress state of a particular layup, with statistical-based convergence and failure criteria considered. Layup optimisation variables include ply thickness, ply angle, fibre-volume fraction and the number of plies. Several tube geometries and thermomechanical loading conditions are investigated to characterise their influence on optimisation convergence and lightweighting outcomes. The PSO approach is shown to successfully optimise lightweighting within a fixed confidence interval for the boundary conditions considered when compared to known results, achieving significant efficiency improvements over alternative algorithms. Additionally, the increased optimisation efficiency enables investigation into the influence of thermomechanical loading conditions on the layup design required to maximise lightweighting. The presence of thermal loading is found to enhance lightweighting over pure mechanical loading by enabling improved stress distribution across tube solutions. When considered without a monolithic liner material, thermal loading establishes a temperature threshold above which tubular composite composites are unable to be integrated without failure due to stress interactions. This study demonstrates the effectiveness of PSO as an approach to improving the efficiency and performance of lightweighting optimisation for composite tubular structures over traditional methods. In addition, several key interactions between the optimised layup and thermomechanical loading conditions are identified. These outcomes will improve the capability of lightweighting optimisation performance and enable better understanding of composite thermomechanical response. [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]

At present, renewable energy sources (RESs) from photovoltaic (PV) and wind turbine (WT) play an important role in electricity generations. On the other hand, battery energy storage systems (BESSs) have been conventionally used to store and supply energy to help smooth power generation. However, the intermittent nature of the RES can cause variations in the power produced by PV and WT generations. The system voltage fluctuations resulting from the changes in electrical power led to voltage stability problems. The on-load tap changer (OLTC) installed on substation transformers to control the voltage level will be overloaded. This excessive operation affects the lifespan of the component, damage, maintenance cost, and system reliability. These problems can be mitigated by effectively controlling the operation of the BESS to reduce voltage oscillations and the OLTC operations. This paper presents the optimal real-time operation of BESS to control system voltages and reduce OLTC operations under the power fluctuations from PV and WT intermittency. The system voltage can be controlled by charging/discharging BESS's active and reactive power using the proposed approach. The optimal BESS operation is formulated as an optimization problem solved by particle swarm optimization (PSO) to minimize the OLTC operations. The PSO algorithm is created as an m-file script in MATLAB for evaluating the optimal solution. The power system modeling and power flow analysis are carried out using a DPL script in DIgSILENT PowerFactory. The simulation case studies are performed using the IEEE 123-node test feeder, modified by BESS, PV, and WT installation. A comparative study is made between the conventional BESS operation for smoothing PV generation, and the proposed optimal BESS approach. The simulation test results show that the proposed approach significantly mitigates the system voltage fluctuation by 59.04% and reduces the number of OLTC operations by 50.0%, compared with those from the conventional BESS operation. [ABSTRACT FROM AUTHOR]

General Path Planning (GPP) is a challenging problem in the field of mobile robotics due to its complexity. The robots must selected their path from the starting point to the target point with the lowest possible distance, in the least possible time, and with the fewest possible turns and movements. The aim of this research is to achieve best path planning of a mobile robot using the hybrid algorithm. This paper proposed heuristic algorithms for determining the optimal pathway of the robot in a static environment. These algorithms are the Particle Swarming Optimization (PSO), the Ant Colony Optimization (ACO), and the hybrid approach of ACO&PSO. They used to obtain the perfect path for the robot as well as to avoid hitting obstacles that it encounters through its path. Initially, each of the two algorithms is implemented separately in a static environment, and then the hybrid one is implemented. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The results obtained for the hybrid algorithm were better than the PSO and ACO algorithms. [ABSTRACT FROM AUTHOR]

Phasor measurement unit (PMU) is a modern device that could be used as both protection and wide area monitoring in a power system. Throughout the wide area monitoring system (WAMS), all the synchronized data acquired by the PMUs in multiple locations were analyzed by phasor data concentrators (PDC). This control support system could determine a suitable protective control and a preventive correction in the case of a contingency plan based on PMUs measurement data. Subsequently, the allocation of PMUs is important due to constraints such as the cost of PMUs itself. Therefore, the objective of most research in this area is to determine the optimal PMUs placement (OPP) that can achieve full observability of the power system with minimal number of PMUs. In this paper, using particle swarm optimization (PSO) algorithm, the effect of PSO parameters is investigated. A binary concept of PSO is used to determine PMUs optimal location for IEEE 30-bus system. The tests were divided into five cases and the simulation results show the importance of parameters used for PSO algorithm. In the tests, the use of linear decreasing inertia weight or constriction factor can prevent the particles from being trapped in local optima. [ABSTRACT FROM AUTHOR]

In recent years, the most utilization of power electronic devices with renewable energy sources increase the harmonic level in current and voltage. Because of harmonics, the delivered power quality is affected. In literature, Power Quality (PQ) mitigation methods are available which fails to enhance the performance at critical load conditions. Hence, in this paper, the Seagull Optimization Algorithm (SOA) based Z Source Inverter (ZSI) is utilized to empower real power by compensating the reactive power requirement in the grid-connected Photovoltaic (PV) structure. The ZSI can be controlled with the assistance of the SOA algorithm which completely mitigates PQ problems. The main objective of the projected methodology is to compensate reactive power transfer and empower real power in a grid-connected PV structure. The projected methodology is utilized to manage the voltage in addition current through the ZSI in the grid- connected PV system. The projectedtechnique is executed in the MATLAB/Simulink platform and performances were evaluated. The proposed methodology can be compared with conventional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO), respectively. The proposed methodology is validated by real and reactive power, voltage, current and frequency. [ABSTRACT FROM AUTHOR]

The paper considers the task allocation problem in the case where there is a small number of agents initialized at a single point. The objective is to achieve an even distribution of agents to tasks. To address this problem, this paper proposes a new method that endows agents with models of motivation and leadership to aid their coordination. The proposed approach uses the Particle Swarm Optimization algorithm with a ring neighborhood topology as a foundation and incorporates computational models of motivation to achieve the goals of task allocation more effectively. Simulation results show that, first, the proposed method increases the number of tasks discovered. Secondly, the number of tasks to which the agents are allocated increases. Thirdly, the agents distribute themselves more evenly among the tasks. [ABSTRACT FROM PUBLISHER]

This paper brings out the studies of generation scheduling problem in an electrical power system. This paper presents some general reviews of research and developments in the field of unit commitment based on published articles and web-sites. Here, it is set about to perform a comprehensive survey of research work made in the domain of Unit Commitment using various techniques. This may be a helpful tool for the researchers, scientists or investigators working in the area of unit commitment. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for Economic Dispatch (ED) problems incorporating wind power plant using Particle Swarm Optimization (PSO) method. As Wind Power Plant increase in power systems, its effects to conventional units should be analyzed. Also the total cost is dependent on wind speed in specific period of time. Therefore, the mathematical techniques are not appropriate to find the global optimum ED. In this paper, PSO is proposed to deal with wind power plants in ED. To show efficiency of wind power plant in reducing total cost, different simulation scenarios with and without wind power production are simulated. [ABSTRACT FROM PUBLISHER]