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Full-Text Articles in Physical Sciences and Mathematics

Feature Selection Optimization With Filtering And Wrapper Methods: Two Disease Classification Cases, Serhat Ati̇k, Tuğba Dalyan Nov 2023

Feature Selection Optimization With Filtering And Wrapper Methods: Two Disease Classification Cases, Serhat Ati̇k, Tuğba Dalyan

Turkish Journal of Electrical Engineering and Computer Sciences

Discarding the less informative and redundant features helps to reduce the time required to train a learning algorithm and the amount of storage required, improving the learning accuracy as well as the quality of results. In this study, we present different feature selection approaches to address the problem of disease classification based on the Parkinson and Cardiac Arrhythmia datasets. For this purpose, first we utilize three filtering algorithms including the Pearson correlation coefficient, Spearman correlation coefficient, and relief. Second, metaheuristic algorithms are compared to find the most informative subset of the features to obtain better classification accuracy. As a final …

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An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu Jul 2023

An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu

Research Collection School Of Computing and Information Systems

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve …

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Research On Unmanned Swarm Combat System Adaptive Evolution Model Simulation, Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma Apr 2023

Research On Unmanned Swarm Combat System Adaptive Evolution Model Simulation, Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma

Journal of System Simulation

Abstract: Aiming at the fact that the intelligent unmanned swarm combat system is mainly composed of large-scale combat individuals with limited behavioral capabilities and has limited ability to adapt to the changes of battlefield environment and combat opponents, a learning evolution method combining genetic algorithm and reinforcement learning is proposed to construct an individual-based unmanned bee colony combat system evolution model. To improve the adaptive evolution efficiency of bee colony combat system, an improved genetic algorithm is proposed to improve the learning and evolution speed of bee colony individuals by using individual-specific mutation optimization strategy. Simulation experiment on …

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Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen Jan 2023

Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen

Research Collection School Of Computing and Information Systems

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated …

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Randomized Algorithms For Resource Allocation In Device To Device Communication., Subhankar Ghosal Dr. Dec 2022

Randomized Algorithms For Resource Allocation In Device To Device Communication., Subhankar Ghosal Dr.

Doctoral Theses

In device to device (D2D) communication, two users residing in close proximity can directly communicate between them, through a common channel, without the need of a base station. A pair of D2D users forms a link and a channel needs to be allocated to it. The interference relationship among the active links at time t is modelled as an interference graph g(t). To establish interference-free communication, we have to assign a channel vector C(t) and a power vector corresponding to the active links such that the required signal to interference plus noise ratio (SINR) is satisfied for each link. Since …

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Design And Optimization Of Nanooptical Couplers Based On Photonic Crystals Involving Dielectric Rods Of Varying Lengths, Şi̇ri̇n Yazar, Özgür Sali̇h Ergül Sep 2022

Design And Optimization Of Nanooptical Couplers Based On Photonic Crystals Involving Dielectric Rods Of Varying Lengths, Şi̇ri̇n Yazar, Özgür Sali̇h Ergül

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents design and optimization of compact and efficient nanooptical couplers involving photonic crystals. Nanooptical couplers that have single and double input ports are designed to obtain efficient transmission of electromagnetic waves in desired directions. In addition, these nanooptical couplers are cascaded by adding one after another to realize electromagnetic transmission systems. In the design and optimization of all these nanooptical couplers, the multilevel fast multipole algorithm, which is an efficient full-wave solution method, is used to perform electromagnetic analyses and simulations. A heuristic optimization method based on genetic algorithms is employed to obtain effective designs that provide the …

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Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve Jun 2022

Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve

Theses and Dissertations

This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred …

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Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie May 2022

Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie

Student Research Symposium

Typical Artificial Neural Networks (ANNs) have static architectures. The number of nodes and their organization must be chosen and tuned for each task. Choosing these values, or hyperparameters, is a bit of a guessing game, and optimizing must be repeated for each task. If the model is larger than necessary, this leads to more training time and computational cost. The goal of this project is to evolve networks that grow according to the task at hand. By gradually increasing the size and complexity of the network to the extent that the task requires, we will build networks that are more …

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Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price Mar 2022

Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price

Theses and Dissertations

Increased levels of time are spent indoors, decreasing human interaction with nature and degrading photoentrainment, the synchronization of circadian rhythms with daylight variation. Military imagery analysts, among other professionals, are required to work in low light level environments to limit power consumption or increase contrast on display screens to improve detail detection. Insufficient exposure to light in these environments results in inadequate photoentrainment which is associated with degraded alertness and negative health effects. Recent research has shown that both the illuminance (i.e., perceived intensity) and wavelength of light affect photoentrainment. Simultaneously, modern lighting technologies have improved our ability to construct …

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Many-Objective Evolutionary Algorithms: Objective Reduction, Decomposition And Multi-Modality., Monalisa Pal Dr. Jan 2022

Many-Objective Evolutionary Algorithms: Objective Reduction, Decomposition And Multi-Modality., Monalisa Pal Dr.

Doctoral Theses

Evolutionary Algorithms (EAs) for Many-Objective Optimization (MaOO) problems are challenging in nature due to the requirement of large population size, difficulty in maintaining the selection pressure towards global optima and inability of accurate visualization of high-dimensional Pareto-optimal Set (in decision space) and Pareto-Front (in objective space). The quality of the estimated set of Pareto-optimal solutions, resulting from the EAs for MaOO problems, is assessed in terms of proximity to the true surface (convergence) and uniformity and coverage of the estimated set over the true surface (diversity). With more number of objectives, the challenges become more profound. Thus, better strategies have …

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Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah Bill, Lance E. Champagne, Bruce Cox, Trevor J. Bihl Apr 2021

Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah Bill, Lance E. Champagne, Bruce Cox, Trevor J. Bihl

Faculty Publications

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued …

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Unified Multi-Objective Genetic Algorithm For Energy Efficient Job Shop Scheduling, Hongjong Wei, Shaobo Li, Huageng Quan, Dacheng Liu, Shu Rao, Chuanjiang Li, Jianjun Hu Apr 2021

Unified Multi-Objective Genetic Algorithm For Energy Efficient Job Shop Scheduling, Hongjong Wei, Shaobo Li, Huageng Quan, Dacheng Liu, Shu Rao, Chuanjiang Li, Jianjun Hu

Faculty Publications

In recent years, people have paid more and more attention to traditional manufacturing’s environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and …

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Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel Jan 2021

Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites, and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving their performance. In this study, prediction performances of neural networks are evaluated and their performances are improved using genetic algorithms. Performances obtained in this study are compared with those of other studies. After that, superiority …

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Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz Jan 2021

Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz

All Works

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can …

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Determining Material Structures And Surface Chemistry By Genetic Algorithms And Quantum Chemical Simulations, Josiah Jesse Roberts Jan 2021

Determining Material Structures And Surface Chemistry By Genetic Algorithms And Quantum Chemical Simulations, Josiah Jesse Roberts

Theses and Dissertations--Chemistry

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and …

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Research And Simulation On Control Algorithm For Multi-Objective Optimization Of Urban Rail Train, Jianjun Meng, Minggao Pei, Wu Fu, Tengzhou Wei, Hao Shuai Jun 2020

Research And Simulation On Control Algorithm For Multi-Objective Optimization Of Urban Rail Train, Jianjun Meng, Minggao Pei, Wu Fu, Tengzhou Wei, Hao Shuai

Journal of System Simulation

Abstract: According to the characteristics of urban rail train running multiple objective, the multi-objective operation model for urban rail train was established with the energy consumption, punctuality, accurate parking and comfort level as the optimization indexes. Genetic algorithms was used to optimize running multi-objective model of urban rail train, and according to train traction calculation and computer simulation, the train running target curve was obtained. The fuzzy control and PID control algorithm were applied to urban rail train system to establish adaptive fuzzy PID controller and PID control in order to track the target curve. Simulation results show that adaptive …

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User Profiling For Tv Program Recommendation Based On Hybrid Televisionstandards Using Controlled Clustering With Genetic Algorithms And Artificial Neuralnetworks, İhsan Topalli, Selçuk Kilinç Jan 2020

User Profiling For Tv Program Recommendation Based On Hybrid Televisionstandards Using Controlled Clustering With Genetic Algorithms And Artificial Neuralnetworks, İhsan Topalli, Selçuk Kilinç

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificial intelligence and the latest technologies developed for the television area is expanded further using controlled clustering with genetic algorithms (CCGA). For this purpose, genetic algorithms (GAs), artificial neural networks (ANNs), and hybrid broadcast broadband television (HbbTV) are combined to get the users' television viewing habits and to create profiles. Then television programs are recommended to the users based on that profiling. The data gathered by the developed HbbTV application for previous studies are reused in this study. These data are employed to cluster users. …

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A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland Jan 2020

A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even …

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Optimization Of Real-World Outdoor Campaign Allocations, Fatmanur Akdoğan Uzun, Doğan Altan, Ercan Peker, Mahmut Altuğ Üstün, Sanem Sariel Jan 2020

Optimization Of Real-World Outdoor Campaign Allocations, Fatmanur Akdoğan Uzun, Doğan Altan, Ercan Peker, Mahmut Altuğ Üstün, Sanem Sariel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we investigate the outdoor campaign allocation problem (OCAP), which asks for the distribution of campaign items to billboards considering a number of constraints. In particular, for a metropolitan city with a large number of billboards, the problem becomes challenging. We propose a genetic algorithm-based method to allocate campaign items effectively, and we compare our results with those of nonlinear integer programming and greedy approaches. Real-world data sets are collected with the given constraints of the price class ratios of billboards located in İstanbul and the budgets of the given campaigns. The methods are evaluated in terms of …

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Accurate Indoor Positioning With Ultra-Wide Band Sensors, Taner Arsan Jan 2020

Accurate Indoor Positioning With Ultra-Wide Band Sensors, Taner Arsan

Turkish Journal of Electrical Engineering and Computer Sciences

Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m …

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Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson Jun 2019

Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson

Electronic Thesis and Dissertation Repository

Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental …

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Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm Jun 2019

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

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Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher May 2019

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

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Neuroevolutional Methods For Decision Support Under Uncertainty, Nina Komleva, Olga Khlopkova, Matthew He Feb 2019

Neuroevolutional Methods For Decision Support Under Uncertainty, Nina Komleva, Olga Khlopkova, Matthew He

Mathematics Faculty Articles

The article presents a comparative analysis of the fundamental neuroevolutional methods, which are widely applied for the intellectualization of the decision making support systems under uncertainty. Based on this analysis the new neuroevolutionary method is introduced. It is intended to modify both the topology and the parameters of the neural network, and not to impose additional constraints on the individual. The results of the experimental evaluation of the performance of the methods based on the series of benchmark tasks of adaptive control, classification and restoration of damaged data are carried out. As criteria of the methods evaluation the number of …

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Rvm Soft Sensing Model Based On Optimized Combined Kernel Function, Yanan Zhang, Huizhong Yang Jan 2019

Rvm Soft Sensing Model Based On Optimized Combined Kernel Function, Yanan Zhang, Huizhong Yang

Journal of System Simulation

Abstract: An RVM spft sensingmodeling method based onthe optimizedcombined kernel functionis proposed.In order to simultaneously get better prediction and sparsity, a fitness function synthesizing regression accuracy and sparsity is created while constructing a combined kernel functionfor RVM.The genetic algorithm is used to optimize the weights and kernel parametersof the RVMcombined kernel.The proposed method is used totomodela cleavage-recovery unit in the production process of Bisphenol-A.The results show that it can guarantee better sparsity andregression accuracy than the general SVM combinedkernel model andGA-RVM single kernel model.

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Ingan/Gan Tandem Solar Cell Parameter Estimation: A Comparative Stud, Abdelmoumene Benayad, Smail Berrah Jan 2019

Ingan/Gan Tandem Solar Cell Parameter Estimation: A Comparative Stud, Abdelmoumene Benayad, Smail Berrah

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, two hybrid estimation approaches, hybrid genetic algorithm (TR-GA) and hybrid particle swarm optimization (TR-PSO), are used to estimate single-diode model InGaN/GaN solar cell parameters from J?V experimental data under AM0 illumination. These parameters are photocurrent density ($J_{ph}$), reverse saturation current density ($J_{s}$), ideality factor ($A$), series resistance ($R_{s}$), and shunt resistance ($R_{sh}$). The trust region (TR) method used in both approaches provides the initial conditions and helps to avoid the problem of premature convergence (due to local minimum). Simulation results based on the minimization of the mean square error between experimental and theoretical J-V characteristics show that …

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Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin Jan 2019

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …

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A Simheuristic Approach For Evolving Agent Behaviour In The Exploration For Novel Combat Tactics, Chiou-Peng Lam, Martin Masek, Luke Kelly, Michael Papasimeon, Lyndon Benke Jan 2019

A Simheuristic Approach For Evolving Agent Behaviour In The Exploration For Novel Combat Tactics, Chiou-Peng Lam, Martin Masek, Luke Kelly, Michael Papasimeon, Lyndon Benke

Research outputs 2014 to 2021

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed …

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Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism For Solving Large-Scale Economic Dispatch With Valve-Point Effects And Multiple Fuel Options, Yude Yang, Bori Wei, Hui Liu, Yiyi Zhang, Junhui Zhao, Emad Manla Aug 2018

Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism For Solving Large-Scale Economic Dispatch With Valve-Point Effects And Multiple Fuel Options, Yude Yang, Bori Wei, Hui Liu, Yiyi Zhang, Junhui Zhao, Emad Manla

Electrical & Computer Engineering and Computer Science Faculty Publications

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The …

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Impact Of Data Selection On The Accuracy Of Atmospheric Refractivity Inversions Performed Over Marine Surfaces, Ian Joseph Matsko Jul 2018

Impact Of Data Selection On The Accuracy Of Atmospheric Refractivity Inversions Performed Over Marine Surfaces, Ian Joseph Matsko

Electronic Theses and Dissertations

Within the Earth’s atmosphere there is a planetary boundary layer that extends from the surface to roughly 1 km above the surface. Within this planetary boundary layer exists the marine atmospheric boundary layer, which is a complex turbulent surface layer that extends from the sea surface to roughly 100 m in altitude. The turbulent nature of this layer combined with the interactions across the air-sea interface cause ever changing environmental conditions within it, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) …

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