Implementation Of Particle Swarm Optimization Research Articles

Power loss minimization by optimal allocation and sizing of STATCOM via particle swarm optimization

The provision of electricity is problematic in remote and rural areas. The distribution system is insufficiently efficient to provide rural areas with electricity. Long distance between the power supply and rural areas is the reason why there is insufficient electricity. Static synchronous compensator (STATCOM) technology is one of the solutions that have more positive effects, such as reducing power losses, and can therefore increase the system’s efficacy. However, the non-optimal allocation and magnitude of STATCOM may increase losses and have a negative impact on power, resulting in a decrease in system efficiency. This paper introduces the Particle Swarm Optimization (PSO) algorithm as the proposed algorithm to optimize the location and capacity of STATCOM in order to reduce power losses in the distribution system. The proposed technique is implemented on a 15-bus IEEE system and simulated using the MATLAB program. The simulation results demonstrate that the optimal location and quantity of the STATCOM proposed by PSO effectively reduces power loss by 6.058% compared to without PSO implementation.

COST-EFFECTIVE ENERGY MANAGEMENT SYSTEMS STRATEGY IN OPTIMIZATION OF PHOTOVOLTAIC FOR GRID-CONNECTED SYSTEM

Renewable Energy Source (RES) based Distributed Generation (DG) like Photovoltaic (PV) is widely integrated into the distribution network, particularly for residential. With proper planning, the installation of optimal PV in the network is capable of minimizing the dependency on the power grid generation. However, the optimum use of solar energy has been limited by the weather and the load variation. At the particular time, the generated PV output is not fully utilized during the minimum load. The excessive generation of PV power occurs and causes an increase in the cost of electricity consumption. Therefore, the purpose of this paper is to optimize the PV size for the grid-connected system considering the Battery Energy Storage System (BESS) and the proper Energy Management System (EMS) Strategy in order to reduce the grid power consumption. BESS is introduced to store the excess PV power generated during peak hours, while the cost-effective EMS strategy is proposed to ensure the RES is fully employed. The number of PV panels is optimized using Particle Swarm Optimization (PSO) technique. The implementation of PSO in optimising PV size can reduce the number of PV panels by 21% compared to the conventional method.

Competitive Pricing for Multiple Market Segments Considering Consumers’ Willingness to Pay

Defining prices and in which consumers’ segments to put the company’s efforts within competitive markets selling bundles is challenging. On the one hand, methodologies focused on competition are usually appropriate for analyzing market dynamics but not for helping decision makers in specific tasks regarding pricing. On the other hand, simplistic cost-oriented methods may fail to capture consumer behavior. We see these characteristics in such markets as telecommunications, retail, and financial service providers, among others. We propose a framework to support pricing decisions for products with multiple attributes in competitive markets, considering consumers’ willingness to pay and multiple segments. The proposed model is a nonlinear profit maximization probabilistic problem. We represent the demands for products and services through a multinomial logit model and then include consumers’ maximum willingness to pay through soft constraints within the demand function. Since the profit function is non-concave, we deal with the nonlinearity and the multiple optima to solve the model through an equivalent nonlinear model and a particle swarm optimization (PSO) heuristic. This setting allows us to find the prices that achieve equilibrium for the game among the firms that maximize their profits. Including the features shown, our approach enables decision makers to set prices optimally. Estimating the parameters needed to run our model requires more effort than traditional multinomial approaches. Nevertheless, we show that it is essential to include these aspects because the optimal prices are different from those obtained with more simplified models that do not have them. Additionally, there are well-established methodologies available to estimate those parameters. Both the determination of the first-order optimality conditions and the PSO implementation allow to find equilibria, quantify the effect of the consumers’ maximum willingness to pay, and assess the competition’s relevance. As complementary material, we analyze a case from a Chilean telecommunications company and show the results regarding price decisions and market share effects. According to our literature review, these aspects have not been handled and quantified jointly, as we do to support pricing.

Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing the use of their resources. The programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources are allocated to tasks. Time or time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for m machine flow shop problems, to Implement Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart.

Implementation of heuristic search algorithms in the calibration of a river hydraulic model

In this contribution, two heuristic search algorithms, (1) particle swarm optimization (PSO) and (2) genetic algorithm (GA) are used to calibrate the iRIC, a two-dimensional hydraulic model for a test case on the Green River in Utah. The goal of the search algorithms was to find the combination of roughness values that minimized model error compared to the measured water surface elevation. An artificial neural network (ANN), which was trained based on the simulated water surface profiles obtained from iRIC, was used to measure the fitness of each solution. Implementation of PSO and GA both improved the accuracy of the calibration by 34 and 30 percent respectively relative to the trial and error. This research demonstrates that the implementation of heuristic search models provides an objective methodology for the calibration of hydraulic models with improved performance when compared to models calibrated via trial and error.

Model Based Demand Order Estimation by Using Optimal Architecture Artificial Neural Network with Metaheuristic Optimizations

With the increase of e-commerce volumes in recent years, it is useful to estimate daily demand order numbers in order to improve the demand forecasts, production-distribution planning and sales services. In this manner, data-driven modeling and machine learning tools have been preferred to enhance demand order predictions, timely delivery, incomes and customer satisfaction in electronic trading because real-time data collection is possible in e-commerce platforms. Artificial Neural Networks (ANNs) are widely used for data-driven modeling and prediction problems. Since affecting the approximation performance of neural network function, the modeling performance of ANNs strongly depends on the architecture of neural networks, and architectural optimization of ANN models has become a main topic in the neuroevolution field. This study presents an architecture optimization method that implements Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to optimize ANN model architecture for the estimation of total demand order numbers from the sparse demand order data. In this approach, PSO and DE algorithm only optimizes neural model architecture according to an effective network search policy and the training of ANN models is carried out by using backpropagation algorithm. This neural architecture model optimization approach considers generalization of data, reducing neuron and training epoch numbers and it can yield an optimal architecture data-driven neural model for estimation of the daily total orders. In the experimental study, optimal architecture ANN models are obtained according to the daily order forecasting dataset.

Implementasi Particle Swarm Optimization untuk Optimasi Fuzzy-Social Force Model pada Sistem Navigasi Robot Omnidirectional

Particle Swarm Optimization (PSO) is a swarm-based optimization method that is easy to implement and requires only a few parameters to set. This study aims to implement PSO to optimize the Fuzzy-Social Force Model (FSFM). FSFM combines the Social Force Model (SFM) as a navigation algorithm and the Fuzzy Inference Rule (FIS) to produce adaptive gain on SFM to create a mobile robot navigation system that is more responsive to obstacles. The PSO implementation optimizes fuzzy rules to be more optimal when the mobile robot navigates into social spaces. From the experimental test results on the VREP simulation software, cognitive parameter c1 = 1 and social parameter c2 = 2 produced the best navigation performance compared to other test parameter values.

Systematic Literature Review Of Particle Swarm Optimization Implementation For Time-Dependent Vehicle Routing Problem

Time-dependent VRP (TDVRP) is one of the three VRP variants that have not been widely explored in research in the field of operational research, while Particle Swarm Optimization (PSO) is an optimization algorithm in the field of operational research that uses many variables in its application. There is much research conducted about TDVRP, but few of them discuss PSO's implementation. This article presented as a literature review which aimed to find a research gap about implementation of PSO to resolve TDVRP cases. The research was conducted in five stages. The first stage, a review protocol defined in the form of research questions and methods to perform the review. The second stage is references searching. The third stage is screening the search result. The fourth stage is extracting data from references based on research questions. The fifth stage is reporting the study literature results. The results obtained from the screening process were 37 eligible reference articles, from 172 search results articles. The results of extraction and analysis of 37 reference articles show that research on TDVRP discusses the duration of travel time between 2 locations. The route optimization parameter is determined from the cost of the trip, including the total distance traveled, the total travel time, the number of routes, and the number used vehicles. The datasets that are used in research consist of 2 types, real-world datasets and simulation datasets. Solomon Benchmark is a simulation dataset that is widely used in the case of TDVRP. Research on PSO in the TDVRP case is dominated by the discussion of modifications to determine random values of PSO variables.

Potential of a Nonperennial Tributary Integrated with Solar Energy for Rural Electrification: A Case Study of Ikukwa Village in Tanzania

This study evaluates the hydropower potential in the design of a micro-hydro/solar photovoltaic hybrid system with battery energy storage for increasing the access to electricity in Ikukwa Village in Mbeya Region of Tanzania. Usually, hybridized hydropower schemes are designed from perennial streams for the provision of electricity. This study incorporates the run-of-the river (COE) power scheme, which originates from the untapped potential of nonperennial hydro-energy source and the use of traditional approach of data measurements for Ikata tributary to design hybrid system. The system is optimized by the minimization of the total net present cost (NPC) and cost of energy (COE) using the soft computing method of Hybrid Optimization of Multiple Energy Resources (HOMER) software and artificial intelligent (AI) techniques. AI optimization techniques such as particle swarm optimization (PSO), grey wolf optimization (GWO), and GWO-PSO hybrid (GWO-PSOHD) algorithms have been employed for further optimal results. The data for solar radiation and the tributary have been obtained from the National Aeronautics and Space Administration (NASA) and traditional methods of measurements, respectively. The estimated maximum water flow rate and head are 2.943 m3/s and 13 m, respectively. In the same period, the approximated theoretical power potential of the tributary is found to be 375 kW. Total NPCs obtained from HOMER, PSO, GWO, and GWO-PSOHD methods are $ 141, 397.76, $ 95 167.21, $ 92 472.82, and $ 91,854.10, respectively. Similarly, the optimal results of COE from HOMER, PSO, GWO, and GWO-PSOHD approaches are $ 0.1818/kWh, $ 0.1185/kWh, $ 0.1182/kWh, and $ 0.1181/kWh, respectively. Comparatively, PSO implementation has indicated the greatest energy cost, while the cost acquired by GWO-PSOHD is the lowest for all aforementioned AI optimization techniques. The tributary under study has a high potential of diversification of energy sources for rural electrification in the area of study and other parts of the world with comparable conditions.

A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making

Consensus is an important issue in group decision making as it aims to avoid future contestation by the decision makers. The elicitation of criteria and decision makers’ weights is an essential part of decision-making problems. This study proposes a new consensus model for group decision making based on dual hesitant fuzzy and evolutionary algorithm for the definition of unknown criteria and decision makers’ weights. The Dual Hesitant Fuzzy Preference Relations combines the advantages of intuitionistic and hesitant fuzzy representations, and it is used to deal with the imprecision in decision makers’ judgments. In order to find decision makers weights to reach a better level of consensus without the need to modify initial assessments, the proposed Genetic Algorithm (GA) is applied. An illustrative application case is presented in a large steel company considering sustainable criteria. An instance generator was proposed to create different scenarios of decision making varying the number of criteria, decision makers, and level of hesitation. Several instances were used in the computational tests for the evaluation of the GA performance. The effectiveness of the proposed GA was verified by comparing its results with the results of the implemented Particle Swarm Optimization (PSO) algorithm. The GA yielded solutions with improved consensus levels in a reasonable runtime, especially for a small or medium number of decision makers.

Comperative Analysis of Weibull Parameter Estimation Using HPSOBA and Standard PSO and Classical Bat Algorithms

The paper benevolences a hybrid metaheuristic optimization algorithm developed for the computation of Weibull probability and cumulative density functions (PDF) and (CDF) respectively. The algorithm is designed through espousing protruding topographies wich are used in the Bat algorithm (BA) and in the Particle Swarm Optimization (PSO) and is used to analyze the figure and measure constraints of Weibull Distribution (WD). The established algorithm is also verified on target test functions. The results and computational time are compared with the results obtained through the implementation of standard PSO and BA. The computed values of the parameters of WD are also verified through the calculation of, index of agreement (AI), mean absolute percentage error (MAPE) coefficient of determination (R2) and root mean square error (RMSE). The results of parameter estimation are also compared with standard PSO and BA for the mentioned region. The comparative analysis of the results of benchmark functions and Weibull parameter estimation reveal the robustness and verstality of the developed algorithm.

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method.

Optimization of the Efficiency of a Michell-Banki Turbine through the Variation of its Geometrical Parameters using a PSO Algorithm

Small-scale hydropower generation can satisfy the needs of communities located near natural sources of flowing water. The operating conditions of a Michell–Banki Turbine (MBT) are relatively easier to meet than those of other types of turbine, making it useful in places where other devices are not suitable. Moreover, MBT efficiency is almost invariable with respect to flow rate conditions. Nevertheless, such efficiency commonly ranges between 70% and 85%, which is lower than that of other water turbines like Turgo, Pelton, or Francis turbine. The objective of this work is to determine the maximum theoretical efficiency of an MBT and its associated geometrical parameters by implementing Particle Swarm Optimization. The results show a higher effectiveness of the mathematical formulation compared with other cases from literature and show the performance of the optimization method proposed in this study in terms of solution and processing time. Finally, a maximum MBT efficiency of 93.3% was achieved.

Number and Distribution of Fiducial Fibers in a Spectroscopic Survey Telescope

To date, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has been in operation for 12 yr. To improve the telescope's astronomical observation accuracy, the original open-loop fiber positioning system of LAMOST is in urgent need of upgrading. The upgrade plan is to install several fiber view cameras (FVCs) around primary mirror B to build a closed-loop feedback control system. The FVCs are 20 m from the focal surface. To reduce a series of errors when the cameras detect the positions of the optical fibers, we designed fiducial fibers on the focal surface to be fiducial points for the cameras. Increasing the number of fiducial fibers can improve the detection accuracy of the FVC system, but it will also certainly reduce the number of fiber positioners that can be used for observation. Therefore, the focus of this paper is how to achieve the quantity and distribution that meet the requirements of system detection. In this paper, we introduce the necessity of using fiducial fibers, propose a method for selecting their number and present several methods for assessing the uniformity of their distribution. Finally, we implement particle swarm optimization to find the best distribution of fiducial fibers.

Swarm Intelligence-based Hierarchical Clustering for Identification of ncRNA using Covariance Search Model

Covariance Model (CM) has been quite effective in finding potential members of existing families of non-coding Ribonucleic Acid (ncRNA) identification and has provided ex-cellent accuracy in genome sequence database. However, it has significant drawbacks with family-specific search. An existing Hierarchical Agglomerative Clustering (HAC) technique merged overlapping sequences which is known as combined CM (CCM). However, the structural information will be discarded, and the sequence features of each family will be significantly diluted as the number of original structures increases. Additionally, it can only find members of the existing families and is not useful in finding potential members of novel ncRNA families. Furthermore, it is also important to construct generic sequence models which can be used to recognize new potential members of novel ncRNA families and define unknown ncRNA sequence as the potential members for known families. To achieve these objectives, this study proposes to implement Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to ensure the CCMs have the best quality for every level of dendrogram hierarchy. This study will also apply distance matrix as the criteria to measure the compatibility between two CMs. The proposed techniques will be using five gene families with fifty sequences from each family from Rfam database which will be divided into training and testing dataset to test CMs combination method. The proposed techniques will be compared to the existing HAC in terms of identification accuracy, sum of bit-scores, and processing time, where each of these performance measurements will be statistically validated.

A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network’s learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model.

Optimization of the Efficiency of a Michell–Banki Turbine Through the Variation of Its Geometrical Parameters Using a PSO Algorith

Small-scale hydropower generation can satisfy the needs of communities located near natural sources of flowing water. The operating conditions of a Michell–Banki Turbine (MBT) are relatively easier to meet than those of other types of turbine, making it useful in places where other devices are not suitable. Moreover, MBT efficiency is almost invariable with respect to flow rate conditions. Nevertheless, such efficiency commonly ranges between 70% and 85%, which is lower than that of other water turbines like Turgo, Pelton, or Francis turbine. The objective of this work is to determine the maximum theoretical efficiency of an MBT and its associated geometrical parameters by implementing Particle Swarm Optimization. The results show a higher effectiveness of the mathematical formulation compared with other cases from literature and show the performance of the optimization method proposed in this study in terms of solution and processing time. Finally, a maximum MBT efficiency of 93.3% was achieved

Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

Palm oil is a reliable vegetable oil producer because the oil produced has advantages than oils from other plants. The amount of Fresh Fruit Bunches (FFB) raw material from Palm oil has a significant impact on the palm oil production process. Therefore, we need a method to forecasting the amount of palm oil (FFB). One of the suitable forecasting methods is fuzzy time series (FTS). However, FTS still has shortcomings such as innacurate determination of the interval length. For this reason, we need to optimize FTS interval to get optimal forecasting. This research implements Particle Swarm Optimization as the optimization method, FTS Chen-Hsu as the forecast method, and Mean Absolute Percentage Error (MAPE) as the measurement of error. The optimization result using PSO produce an error value of 2.0262% smaller than FTS 3.7108%.

Optimal design of brace-viscous damper and pendulum tuned mass damper using Particle Swarm Optimization

We implemented Particle Swarm Optimization (PSO) to determine the most optimal properties of the brace-viscous damper system (BVDS), pendulum tuned mass damper (PTMD), and combined BVDS-PTMD system in mitigation of dynamic response of a jacket platform. To do this, a scaled prototype of an offshore jacket is numerically simulated in ANSYS subjected to ocean waves. Concerning BVDS, at each story, the variables being optimized are (1) BVDS configuration such as chevron, toggle, diagonal; (2) damping coefficient, and (3) brace area. The properties of PTMD being optimized are the damping ratio (ξd), frequency ratio (β), structural excitation frequency ratio (α), and mass ratio (m¯). To do the optimization, the results of ANSYS are exploited to calculate the PSO cost function (standard deviation of deck displacements (σsd)). Regarding the deck displacement and base shear force, the optimization results proved that the optimal BVDS-PTMD combined system outperforms the optimal BVDS and the optimal BVDS performance is better than that of the PTMD system. Both in the optimal BVDS and integrated BVDS-PTMD, the chevron configuration for the top floor and toggle configuration for the first to third floors are evaluated as the optimum arrangement by the optimization algorithm.