Search results for: invasive weed optimization algorithm

Authors: Mohamed Belhorma, Aboubakar S. Bouchikhi, Belkacem Bounab

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This paper addresses modeling a Double A-Arm suspension, a three-dimensional nonlinear model has been developed using the multibody systems formalism. Dynamical study of the different components responses was done, particularly for the wheel assembly. To validate those results, the system was constructed and simulated by RecurDyn, a professional multibody dynamics simulation software. The model has been used as the Objectif function in an optimization algorithm for ride quality improvement.

Keywords: double A-Arm suspension, multibody systems, ride quality optimization, dynamic simulation

Procedia PDF Downloads 137

Authors: Efrain Rodriguez, Sergio Pertuz, Cristhian Riano

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Tool-path generation is an essential step in the FFF (Fused Filament Fabrication)-based Additive Manufacturing (AM) process planning. In the manufacture of a mechanical part by using additive processes, high resource consumption and prolonged production times are inherent drawbacks of these processes mainly due to non-optimized tool-path generation. In this work, we propose a heuristic-search intelligent algorithm-based approach for optimized tool-path generation for FFF-based AM. The main benefit of this approach is a significant reduction of travels without material deposition when the AM machine performs moves without any extrusion. The optimization method used reduces the number of travels without extrusion in comparison with commercial software as Slic3r or Cura Engine, which means a reduction of production time.

Keywords: additive manufacturing, tool-path optimization, fused filament fabrication, process planning

Procedia PDF Downloads 441

Authors: Muhammad Haroon, LI Xiangju

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C. communis L. is an important weed of many crop, but very little information about the adaptation strategies of C. communis L. biotypes under drought stress. We investigated five biotypes of C. communis L under drought stress to identify the adaptation mechanism. The expression of drought stress related genes (DRS1, EREB and HRB1) was up-regulated in biotypes, while in some biotypes their expression was down regulated. All five biotypes can thus regulate water balance to consume less water to maintain their status under drought stress condition. This result concluded that C. communis L. biotypes can survive longer under drought stress condition. Weed scientist should seek more effective management strategies to deal with C. communis L.

Keywords: C. communis, biotypes, drought stress, gene expression

Procedia PDF Downloads 154

Authors: Yue-Tzu Yang, Shu-Ching Liao

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This study presents the numerical simulation of three-dimensional incompressible steady and laminar fluid flow and conjugate heat transfer of a trapezoidal microchannel heat sink using water as a cooling fluid in a silicon substrate. Navier-Stokes equations with conjugate energy equation are discretized by finite-volume method. We perform numerical computations for a range of 50 ≦ Re ≦ 600, 0.05W ≦ P ≦ 0.8W, 20W/cm2 ≦ ≦ 40W/cm2. The present study demonstrates the numerical optimization of a trapezoidal microchannel heat sink design using the response surface methodology (RSM) and the genetic algorithm method (GA). The results show that the average Nusselt number increases with an increase in the Reynolds number or pumping power, and the thermal resistance decreases as the pumping power increases. The thermal resistance of a trapezoidal microchannel is minimized for a constant heat flux and constant pumping power.

Keywords: microchannel heat sinks, conjugate heat transfer, optimization, genetic algorithm method

Procedia PDF Downloads 318

Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

Procedia PDF Downloads 137

Authors: H. Naseh, M. Mirshams, M. Mirdamadian, H. R. Fazeley

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This paper presents an extension of fuzzy-genetic algorithm multi-objective optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. In many cases, decision making on design variables conflicts with more than one discipline in system design. In space launch system conceptual design, decision making on some design variable (e.g. oxidizer to fuel mass flow rate O/F) in early stages of the design process is related to objective of liquid propellant engine (specific impulse) and Tanks (structure weight). Then, the primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and cylindrical stiffened tank with the minimum weight. To this end, the design problem is established the fuzzy rule set based on designer's expert knowledge with a holistic approach. The independent design variables in this model are oxidizer to fuel mass flow rate, thickness of stringers, thickness of rings, shell thickness. To handle the mentioned problems, a fuzzy-genetic algorithm multi-objective optimization methodology is developed based on Pareto optimal set. Consequently, this methodology is modeled with the one stage of space launch system to illustrate accuracy and efficiency of proposed methodology.

Keywords: cylindrical stiffened tanks, multi-objective, genetic algorithm, fuzzy approach

Procedia PDF Downloads 654

Authors: Kay Thi Yar, Khin Mar Lar Tun

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Nowadays, social networks are so popular and widely used in all over the world. In addition, searching personal information of each person and searching connection between them (peoples’ relation in real world) becomes interesting issue in our society. In this paper, we propose a framework with three portions for exploring peoples’ relations from their connected information. The first portion focuses on the Graph database structure to store the connected data of peoples’ information. The second one proposes the graph database searching algorithm, the Modified-SoS-ACO (Sense of Smell-Ant Colony Optimization). The last portion proposes the Deductive Reasoning Algorithm to define two persons’ relationship. This study reveals the proper storage structure for connected information, graph searching algorithm and deductive reasoning algorithm to predict and analyze the personnel relationship from peoples’ relation in their connected information.

Keywords: personnel information, graph storage structure, graph searching algorithm, deductive reasoning algorithm

Procedia PDF Downloads 450

Authors: Khaled Ben Oualid Medani, Samir Sayah

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The Optimal Reactive Power Dispatch (ORPD) problem has been solved and analysed usually in the normal conditions. However, network collapses appear in contingency conditions. In this paper, ORPD under several contingencies is presented using the proposed method WOA. To ensure viability of the power system in contingency conditions, several critical cases are simulated in order to prevent and prepare the power system to face such situations. The results obtained are carried out in IEEE 30 bus test system for the solution of ORPD problem in which control of bus voltages, tap position of transformers and reactive power sources are involved. Moreover, another method, namely, Particle Swarm Optimization with Time Varying Acceleration Coefficient (PSO-TVAC) has been compared with the proposed technique. Simulation results indicate that the proposed WOA gives remarkable solution in terms of effectiveness in case of outages.

Keywords: optimal reactive power dispatch, power system analysis, real power loss minimization, contingency condition, metaheuristic technique, whale optimization algorithm

Procedia PDF Downloads 119

Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

Procedia PDF Downloads 290

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

Procedia PDF Downloads 110

Authors: Babar Sultan, Kiran Sultan, Waseem Khan, Ijaz Mansoor Qureshi

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In this paper, a novel idea for the performance enhancement of secondary network is proposed for Underlay Cognitive Radio Networks (CRNs). In Underlay CRNs, primary users (PUs) impose strict interference constraints on the secondary users (SUs). The proposed scheme is based on Artificial Bee Colony (ABC) optimization for relay selection and power allocation to handle the highlighted primary challenge of Underlay CRNs. ABC is a simple, population-based optimization algorithm which attains global optimum solution by combining local search methods (Employed and Onlooker Bees) and global search methods (Scout Bees). The proposed two-phase relay selection and power allocation algorithm aims to maximize the signal-to-noise ratio (SNR) at the destination while operating in an underlying mode. The proposed algorithm has less computational complexity and its performance is verified through simulation results for a different number of potential relays, different interference threshold levels and different transmit power thresholds for the selected relays.

Keywords: artificial bee colony, underlay spectrum sharing, cognitive radio networks, amplify-and-forward

Procedia PDF Downloads 581

Authors: Sadegh Sadeghi, Negar Shabani

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From a thermal point of view, zeotropic mixtures are likely to be more efficient than azeotropic fluids in low-temperature thermodynamic cycles due to their suitable boiling characteristics. In this study, performance of a low-temperature Kalina cycle with R717/water working fluid used in different existing power plants is mathematically investigated. To analyze the behavior of the cycle, mass conservation, energy conservation, and exergy balance equations are presented. With regard to the similarity in molar mass of R717 (17.03 gr/mol) and water (18.01 gr/mol), there is no need to alter the size of Kalina system components such as turbine and pump. To optimize the cycle energy and exergy efficiencies simultaneously, a constrained multi-objective optimization is carried out applying an Artificial Bee Colony algorithm. The main motivation behind using this algorithm lies on its robustness, reliability, remarkable precision and high–speed convergence rate in dealing with complicated constrained multi-objective problems. Convergence rates of the algorithm for calculating the optimal energy and exergy efficiencies are presented. Subsequently, due to the importance of exergy concept in Kalina cycles, exergy destructions occurring in the components are computed. Finally, the impacts of pressure, temperature, mass fraction and mass flow rate on the energy and exergy efficiencies are elaborately studied.

Keywords: artificial bee colony algorithm, binary zeotropic mixture, constrained multi-objective optimization, energy efficiency, exergy efficiency, Kalina cycle

Procedia PDF Downloads 152

Authors: Muhammad Ather Nadeem, Bilal Ahmad Khan, Hussam F. Najeeb Alawadi, Athar Mahmood, Aneela Nijabat, Tasawer Abbas, Muhammad Habib, Abdullah

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The global prevalence of Avena fatua infestation poses a significant challenge to wheat sustainability. While chemical control stands out as an efficient and rapid way to control weeds, concerns over developing resistance in weeds and environmental pollution have led to criticisms of herbicide use. Consequently, this study was designed to address these challenges through the chemical synthesis, characterization, and optimization of chitosan-based nanoparticles containing clodinofop Propargyl and fenoxaprop-P-ethyl for the effective management of A. fatua. Utilizing the ionic gelification technique, chitosan-based nanoparticles of clodinofop Propargyl and fenoxaprop-P-ethyl were prepared. These nanoparticles were applied at the 3-4 leaf stage of Phalaris minor weed, applying seven altered doses. These nanoparticles were applied at the 3-4 leaf stage of Phalaris minor weed, applying seven altered doses (D0 (Check weeds), D1 (Recommended dose of traditional-herbicide (TH), D2 (Recommended dose of Nano-herbicide (NPs-H)), D3 (NPs-H with 05-fold lower dose), D4 ((NPs-H) with 10-fold lower dose), D5 (NPs-H with 15-fold lower dose), and D6 (NPs-H with 20-fold lower dose)). Characterization of the chitosan-containing herbicide nanoparticles (CHT-NPs) was conducted using FT-IR analysis, demonstrating a perfect match with standard parameters. UV–visible spectrum further revealed absorption peaks at 310 nm for NPs of clodinofop propargyl and at 330 nm for NPs of fenoxaprop-p-ethyl. This research aims to contribute to sustainable weed management practices by addressing the challenges associated with chemical herbicide use. The application of chitosan-based nanoparticles (CHT-NPs) containing fenoxaprop-P-ethyl and clodinofop-propargyl at the recommended dose of the standard herbicide resulted in 100% mortality and visible injury to weeds. Surprisingly, when applied at a lower dose with 5-folds, these chitosan-containing nanoparticles of clodinofop Propargyl and fenoxaprop-P-ethyl demonstrated extreme control efficacy. Furthermore, at a 10-fold lower dose compared to standard herbicides and the recommended dose of clodinofop-propargyl and fenoxaprop-P-ethyl, the chitosan-based nanoparticles exhibited comparable effects on chlorophyll content, visual injury (%), mortality (%), plant height (cm), fresh weight (g), and dry weight (g) of A. fatua. This study indicates that chitosan/tripolyphosphate-loaded nanoparticles containing clodinofop-propargyl and fenoxaprop-P-ethyl can be effectively utilized for the management of A. fatua at a 10-fold lower dose, highlighting their potential for sustainable and efficient weed control.

Keywords: mortality, chitosan-based nanoparticles, visual injury, chlorophyl contents, 5-fold lower dose.

Procedia PDF Downloads 55

Authors: Khaled M. El Naggar

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This paper introduces a digital method for fault section identification in transmission lines. The method uses digital set of the measured short circuit current to locate faults in electrical power systems. The digitized current is used to construct a set of overdetermined system of equations. The problem is then constructed and solved using the proposed digital optimization technique to find the fault distance. The proposed optimization methodology is an application of simulated annealing optimization technique. The method is tested using practical case study to evaluate the proposed method. The accurate results obtained show that the algorithm can be used as a powerful tool in the area of power system protection.

Keywords: optimization, estimation, faults, measurement, high voltage, simulated annealing

Procedia PDF Downloads 391

Authors: Jaber Nikpouri, Arsalan Amralizadeh

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In robotics manipulators, the trajectory should be optimum, thus the torque of the robot can be minimized in order to save power. This paper includes an optimal path planning scheme for a robotic manipulator. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications. In this paper, the improved BBO algorithm is used to minimize the objective function in the presence of different obstacles. The simulation represents that the proposed optimal path planning method has satisfactory performance.

Keywords: biogeography-based optimization, path planning, obstacle detection, robotic manipulator

Procedia PDF Downloads 298

Authors: Y. Wang

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The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is O(CN_maxn²) where C is the iterations, N_max is the maximum number of frequency quadrilaterals containing each edge and n is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5n edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.

Keywords: frequency quadrilateral, iterative algorithm, sparse graph, traveling salesman problem

Procedia PDF Downloads 231

Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 546

Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

Procedia PDF Downloads 229

Authors: Guilherme Baldo Carlos

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The flexible job-shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem, which can be applied to model several applications in a wide array of industries. This problem will have its importance increase due to the shift in the production mode that modern society is going through. The demands are increasing and for products personalized and customized. This work aims to apply a meta-heuristic called a genetic algorithm (GA) to solve this problem. A GA is a meta-heuristic inspired by the natural selection of Charles Darwin; it produces a population of individuals (solutions) and selects, mutates, and mates the individuals through generations in order to find a good solution for the problem. The results found indicate that the GA is suitable for FJSP solving.

Keywords: genetic algorithm, evolutionary algorithm, scheduling, flexible job-shop scheduling

Procedia PDF Downloads 146

Authors: Mustafa Sengul, Enes Sahin, Sertac Arslan

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Pumpjets are one of the marine propulsion systems frequently used in underwater vehicles nowadays. The reasons for frequent use of pumpjet as a propulsion system are that it has higher relative efficiency at high speeds, better cavitation, and acoustic performance than its rivals. Pumpjets are composed of rotor, stator, and duct, and there are two different types of pumpjet configurations depending on the desired hydrodynamic characteristic, which are with accelerating and decelerating duct. Pumpjet with an accelerating channel is used at cargo ships where it works at low speeds and high loading conditions. The working principle of this type of pumpjet is to maximize the thrust by reducing the pressure of the fluid through the channel and throwing the fluid out from the channel with high momentum. On the other hand, for decelerating ducted pumpjets, the main consideration is to prevent the occurrence of the cavitation phenomenon by increasing the pressure of the fluid about the rotor region. By postponing the cavitation, acoustic noise naturally falls down, so decelerating ducted systems are used at noise-sensitive vehicle systems where acoustic performance is vital. Therefore, duct design becomes a crucial step during pumpjet design. This study, it is aimed to optimize the duct geometry of a decelerating ducted pumpjet for a highly speed underwater vehicle by using proper optimization tools. The target output of this optimization process is to obtain a duct design that maximizes fluid pressure around the rotor region to prevent from cavitation and minimizes drag force. There are two main optimization techniques that could be utilized for this process which are parameter-based optimization and gradient-based optimization. While parameter-based algorithm offers more major changes in interested geometry, which makes user to get close desired geometry, gradient-based algorithm deals with minor local changes in geometry. In parameter-based optimization, the geometry should be parameterized first. Then, by defining upper and lower limits for these parameters, design space is created. Finally, by proper optimization code and analysis, optimum geometry is obtained from this design space. For this duct optimization study, a commercial codedparameter-based optimization algorithm is used. To parameterize the geometry, duct is represented with b-spline curves and control points. These control points have x and y coordinates limits. By regarding these limits, design space is generated.

Keywords: pumpjet, decelerating duct design, optimization, underwater vehicles, cavitation, drag minimization

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Authors: Ekin Nurbaş

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One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

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Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

Procedia PDF Downloads 326

Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

Procedia PDF Downloads 249

Authors: Gamal Shiha, Waleed Samir, Zahid Azam, Premashis Kar, Saeed Hamid, Shiv Sarin

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A simple, rapid and non-invasive electromagnetic sensor (C-FAST device) was- patented; for diagnosis of HCV RNA. Aim: To test the validity of the device compared to standard HCV PCR. Subjects and Methods: The first phase was done as pilot in Egypt on 79 participants; the second phase was done in five centers: one center from Egypt, two centers from Pakistan and two centers from India (800, 92 and 113 subjects respectively). The third phase was done nationally as multicenter study on (1600) participants for ensuring its representativeness. Results: When compared to PCR technique, C-FAST device revealed sensitivity 95% to 100%, specificity 95.5% to 100%, PPV 89.5% to 100%, NPV 95% to 100% and positive likelihood ratios 21.8% to 38.5%. Conclusion: It is practical evidence that HCV nucleotides emit electromagnetic signals that can be used for its identification. As compared to PCR, C-FAST is an accurate, valid and non-invasive device.

Keywords: C-FAST- a valid and reliable device, distant cellular interaction, electromagnetic signal detection, non-invasive diagnosis of HCV

Procedia PDF Downloads 431

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

Procedia PDF Downloads 323

Authors: Majid Khalili

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This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms.

Keywords: no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness

Procedia PDF Downloads 416

Authors: M. M. Othman, Walid El-Khattam, Y. G. Hegazy, A. Y. Abdelaziz

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This paper presents a method for finding the optimal location and capacity of dispatchable DGs connected to the distribution feeders for optimal planning for a specified power loss without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37-nodes feeder. The results that are validated by comparing it with results obtained from other competing methods show the effectiveness, accuracy and speed of the proposed method.

Keywords: distributed generators, firefly technique, optimization, power loss

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Authors: Ali Ravangard, S. Mohammadi

Abstract:

Shunt Capacitors have several uses in the electric power systems. They are utilized as sources of reactive power by connecting them in line-to-neutral. Electric utilities have also connected capacitors in series with long lines in order to reduce its impedance. This is particularly common in the transmission level, where the lines have length in several hundreds of kilometers. However, this post will generally discuss shunt capacitors. In distribution systems, shunt capacitors are used to reduce power losses, to improve voltage profile, and to increase the maximum flow through cables and transformers. This paper presents a new method to determine the optimal locations and economical sizing of fixed and/or switched shunt capacitors with a view to power losses reduction and voltage stability enhancement. For solving the problem, a new enhanced cuckoo optimization algorithm is presented.The proposed method is tested on distribution test system and the results show that the algorithm suitable for practical implementation on real systems with any size.

Keywords: capacitor placement, power losses, voltage stability, radial distribution systems

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Authors: Santosh Kumar Suman, Vinod Kumar Giri

Abstract:

The goal of this paper is to outline a speed controller of a DC motor by choice of a PID parameters utilizing genetic algorithms (GAs), the DC motor is extensively utilized as a part of numerous applications such as steel plants, electric trains, cranes and a great deal more. DC motor could be represented by a nonlinear model when nonlinearities such as attractive dissemination are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The DC motor is considered as third order system. Objective of this paper three type of tuning techniques for PID parameter. In this paper, an independently energized DC motor utilizing MATLAB displaying, has been outlined whose velocity might be examined utilizing the Proportional, Integral, Derivative (KP, KI , KD) addition of the PID controller. Since, established controllers PID are neglecting to control the drive when weight parameters be likewise changed. The principle point of this paper is to dissect the execution of optimization techniques viz. The Genetic Algorithm (GA) for improve PID controllers parameters for velocity control of DC motor and list their points of interest over the traditional tuning strategies. The outcomes got from GA calculations were contrasted and that got from traditional technique. It was found that the optimization techniques beat customary tuning practices of ordinary PID controllers.

Keywords: DC motor, PID controller, optimization techniques, genetic algorithm (GA), objective function, IAE

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Authors: Aniket Ganvir, Ritarani Sahu, Suchismita Chinara

Abstract:

The rapid digitization of various aspects of life is leading to the creation of smart IoT ecosystems, where interconnected devices generate significant amounts of valuable data. However, these IoT devices face constraints such as limited computational resources and bandwidth. Cloud computing emerges as a solution by offering ample resources for offloading tasks efficiently despite introducing latency issues, especially for time-sensitive applications like fog computing. Fog computing (FC) addresses latency concerns by bringing computation and storage closer to the network edge, minimizing data travel distance, and enhancing efficiency. Offloading tasks to fog nodes or the cloud can conserve energy and extend IoT device lifespan. The offloading process is intricate, with tasks categorized as full or partial, and its optimization presents an NP-hard problem. Traditional greedy search methods struggle to address the complexity of task offloading efficiently. To overcome this, the efficient crow search algorithm (ECSA) has been proposed as a meta-heuristic optimization algorithm. ECSA aims to effectively optimize computation offloading, providing solutions to this challenging problem.

Keywords: IoT, fog computing, task offloading, efficient crow search algorithm

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