Abstract: Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.
Abstract: The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission.
Abstract: Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution.
Abstract: This paper presents hierarchical operation strategies which are minimizing operation error between day ahead operation plan and real time operation. Operating power systems between centralized and decentralized approaches can be represented as hierarchical control scheme, featured as primary control, secondary control and tertiary control. Primary control is known as local control, featuring fast response. Secondary control is referred to as microgrid Energy Management System (EMS). Tertiary control is responsible of coordinating the operations of multi-microgrids. In this paper, we formulated 3 stage microgrid operation strategies which are similar to hierarchical control scheme. First stage is to set a day ahead scheduled output power of Battery Energy Storage System (BESS) which is only controllable source in microgrid and it is optimized to minimize cost of exchanged power with main grid using Particle Swarm Optimization (PSO) method. Second stage is to control the active and reactive power of BESS to be operated in day ahead scheduled plan in case that State of Charge (SOC) error occurs between real time and scheduled plan. The third is rescheduling the system when the predicted error is over the limited value. The first stage can be compared with the secondary control in that it adjusts the active power. The second stage is comparable to the primary control in that it controls the error in local manner. The third stage is compared with the secondary control in that it manages power balancing. The proposed strategies will be applied to one of the buildings in Electronics and Telecommunication Research Institute (ETRI). The building microgrid is composed of Photovoltaic (PV) generation, BESS and load and it will be interconnected with the main grid. Main purpose of that is minimizing operation cost and to be operated in scheduled plan. Simulation results support validation of proposed strategies.
Abstract: This article presents a method of using the one
dimensional piezo-electric patch on beam model for structural
identification. A hybrid element constituted of one dimensional
beam element and a PZT sensor is used with reduced material
properties. This model is convenient and simple for identification
of beams. Accuracy of this element is first verified against a
corresponding 3D finite element model (FEM). The structural
identification is carried out as an inverse problem whereby
parameters are identified by minimizing the deviation between
the predicted and measured voltage response of the patch, when
subjected to excitation. A non-classical optimization algorithm
Particle Swarm Optimization is used to minimize this objective
function. The signals are polluted with 5% Gaussian noise to
simulate experimental noise. The proposed method is applied on
beam structure and identified parameters are stiffness and damping.
The model is also validated experimentally.
Abstract: The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.
Abstract: Particle swarm optimization (PSO) is becoming one of
the most important swarm intelligent paradigms for solving global
optimization problems. Although some progress has been made to
improve PSO algorithms over the last two decades, additional work
is still needed to balance parameters to achieve better numerical
properties of accuracy, efficiency, and stability. In the optimal
PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor,
respectively. By the same token, the same optimal weightings have
been applied for intensification searches and diversification searches,
respectively. Perturbation and constriction effects are optimally
balanced. Simulations of the de Jong, the Rosenbrock, and the
Griewank functions show that the optimal PSO algorithm indeed
achieves better numerical properties and outperforms the canonical
PSO algorithm.
Abstract: In this paper, a PSO based fractional order PID (FOPID) controller is proposed for concentration control of an isothermal Continuous Stirred Tank Reactor (CSTR) problem. CSTR is used to carry out chemical reactions in industries, which possesses complex nonlinear dynamic characteristics. Particle Swarm Optimization algorithm technique, which is an evolutionary optimization technique based on the movement and intelligence of swarm is proposed for tuning of the controller for this system. Comparisons of proposed controller with conventional and fuzzy based controller illustrate the superiority of proposed PSO-FOPID controller.
Abstract: Phonocardiography is important in appraisal of
congenital heart disease and pulmonary hypertension as it reflects the
duration of right ventricular systoles. The systolic murmur in patients
with intra-cardiac shunt decreases as pulmonary hypertension
develops and may eventually disappear completely as the pulmonary
pressure reaches systemic level. Phonocardiography and auscultation
are non-invasive, low-cost, and accurate methods to assess heart
disease. In this work an objective signal processing tool to extract
information from phonocardiography signal using Wavelet is
proposed to classify the murmur as normal or abnormal. Since the
feature vector is large, a Binary Particle Swarm Optimization (PSO)
with mutation for feature selection is proposed. The extracted
features improve the classification accuracy and were tested across
various classifiers including Naïve Bayes, kNN, C4.5, and SVM.
Abstract: This paper presents the performance of Integrated
Bacterial Foraging Optimization and Particle Swarm Optimization
(IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired
algorithm, which simulates the foraging behavior of bacteria.
It is effectively applied in improving the routing performance in
MANET. In results, it is proved that the PSO integrated with BFO
reduces routing delay, energy consumption and communication
overhead.
Abstract: This study, for its research subjects, uses patients who
had undergone total knee replacement surgery from the database of the
National Health Insurance Administration. Through the review of
literatures and the interviews with physicians, important factors are
selected after careful screening. Then using Cross Entropy Method,
Genetic Algorithm Logistic Regression, and Particle Swarm
Optimization, the weight of each factor is calculated and obtained. In
the meantime, Excel VBA and Case Based Reasoning are combined
and adopted to evaluate the system. Results show no significant
difference found through Genetic Algorithm Logistic Regression and
Particle Swarm Optimization with over 97% accuracy in both
methods. Both ROC areas are above 0.87. This study can provide
critical reference to medical personnel as clinical assessment to
effectively enhance medical care quality and efficiency, prevent
unnecessary waste, and provide practical advantages to resource
allocation to medical institutes.
Abstract: This paper deals with the problem of automatic rule
generation for fuzzy systems design. The proposed approach is based
on hybrid artificial bee colony (ABC) optimization and weighted least
squares (LS) method and aims to find the structure and parameters of
fuzzy systems simultaneously. More precisely, two ABC based fuzzy
modeling strategies are presented and compared. The first strategy
uses global optimization to learn fuzzy models, the second one
hybridizes ABC and weighted least squares estimate method. The
performances of the proposed ABC and ABC-LS fuzzy modeling
strategies are evaluated on complex modeling problems and compared
to other advanced modeling methods.
Abstract: Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Abstract: Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: Consider the Gregory integration (G) formula
with end corrections where h Δ is the forward difference operator with step size h. In this study we prove that can be optimized by
minimizing some of the coefficient k a in the remainder term by
particle swarm optimization. Experimental tests prove that can be rendered a powerful formula for library use.
Abstract: Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images, may cause misalignments. To handle with this limitation, we propose to extract the salient edges and extracted control points (CP) of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT) that finds the best feature points. The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). The experimental results showed that the proposed method produces totally accurate performance for registration medical CT-MR images.
Abstract: This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance.
Abstract: This paper shows the results obtained in the analysis
of the impact of distributed generation (DG) on distribution losses
and presents a new algorithm to the optimal allocation of distributed
generation resources in distribution networks. The optimization is
based on a Hybrid Genetic Algorithm and Particle Swarm
Optimization (HGAPSO) aiming to optimal DG allocation in
distribution network. Through this algorithm a significant
improvement in the optimization goal is achieved. With a numerical
example the superiority of the proposed algorithm is demonstrated in
comparison with the simple genetic algorithm.
Abstract: Routing in MANET is extremely challenging because
of MANETs dynamic features, its limited bandwidth, frequent
topology changes caused by node mobility and power energy
consumption. In order to efficiently transmit data to destinations, the
applicable routing algorithms must be implemented in mobile ad-hoc
networks. Thus we can increase the efficiency of the routing by
satisfying the Quality of Service (QoS) parameters by developing
routing algorithms for MANETs. The algorithms that are inspired by
the principles of natural biological evolution and distributed
collective behavior of social colonies have shown excellence in
dealing with complex optimization problems and are becoming more
popular. This paper presents a survey on few meta-heuristic
algorithms and naturally-inspired algorithms.