The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.

[ES] La búsqueda novedosa es un nuevo paradigma de los algoritmos de optimización, evolucionarios y bioinspirados, que está basado en la idea de forzar la búsqueda del óptimo global en aquellas partes inexploradas del dominio de la función que no son atractivas para el algoritmo, con la intención de evitar estancamientos en óptimos locales. La búsqueda novedosa se ha aplicado al algoritmo de optimización de enjambre de partículas, obteniendo un nuevo algoritmo denominado algoritmo de enjambre novedoso (NS). NS se ha aplicado al conjunto de pruebas sintéticas CEC2005, comparando los resultados con los obtenidos por otros algoritmos del estado del arte. Los resultados muestran un mejor comportamiento de NS en funciones altamente no lineales, a cambio de un aumento en la complejidad computacional. En lo que resta de trabajo, el algoritmo NS se ha aplicado en diferentes modelos, específicamente en el diseño de un motor de combustión interna, en la estimación de demanda de energía mediante gramáticas de enjambre, en la evolución del cáncer de vejiga de un paciente concreto y en la evolución del COVID-19. Cabe remarcar que, en el estudio de los modelos de COVID-19, se ha tenido en cuenta la incertidumbre, tanto de los datos como de la evolución de la enfermedad. / [CA] La cerca nova és un nou paradigma dels algoritmes d'optimització, evolucionaris i bioinspirats, que està basat en la idea de forçar la cerca de l'òptim global en les parts inexplorades del domini de la funció que no són atractives per a l'algoritme, amb la intenció d'evitar estancaments en òptims locals. La cerca nova s'ha aplicat a l'algoritme d'optimització d'eixam de partícules, obtenint un nou algoritme denominat algoritme d'eixam nou (NS). NS s'ha aplicat al conjunt de proves sintètiques CEC2005, comparant els resultats amb els obtinguts per altres algoritmes de l'estat de l'art. Els resultats mostren un millor comportament de NS en funcions altament no lineals, a canvi d'un augment en la complexitat computacional. En el que resta de treball, l'algoritme NS s'ha aplicat en diferents models, específicament en el disseny d'un motor de combustió interna, en l'estimació de demanda d'energia mitjançant gramàtiques d'eixam, en l'evolució del càncer de bufeta d'un pacient concret i en l'evolució del COVID-19. Cal remarcar que, en l'estudi dels models de COVID-19, s'ha tingut en compte la incertesa, tant de les dades com de l'evolució de la malaltia. / [EN] Novelty Search is a recent paradigm in evolutionary and bio-inspired optimization algorithms, based on the idea of forcing to look for those unexplored parts of the domain of the function that might be unattractive for the algorithm, with the aim of avoiding stagnation in local optima. Novelty Search has been applied to the Particle Swarm Optimization algorithm, obtaining a new algorithm named Novelty Swarm (NS). NS has been applied to the CEC2005 benchmark, comparing its results with other state of the art algorithms. The results show better behaviour in high nonlinear functions at the cost of increasing the computational complexity. During the rest of the thesis, the NS algorithm has been used in different models, specifically the design of an Internal Combustion Engine, the prediction of energy demand estimation with Grammatical Swarm, the evolution of the bladder cancer of a specific patient and the evolution of COVID-19. It is also remarkable that, in the study of COVID-19 models, uncertainty of the data and the evolution of the disease has been taken in account. / Martínez Rodríguez, D. (2021). Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178994 / TESIS

Indiana University-Purdue University Indianapolis (IUPUI) / As our population ages, treating bone and joint ailments is becoming increasingly important. Both osteoporosis, a bone disease characterized by a decreased density of mineral in bone, and osteoarthritis, a joint disease characterized by the degeneration of cartilage on the ends of bones, are major causes of decreased movement ability and increased pain. To combat these diseases, many treatments are offered, including drugs and exercise, and much biomedical research is being conducted. However, how can we get the most out of the research we perform and the treatment we do have? One approach is through computational analysis and mathematical modeling. In this thesis, quantitative methods of analysis are applied in different ways to two systems: osteoporosis and osteoarthritis. A mouse model simulating osteoporosis is treated with salubrinal and knee loading. The bone and cell data is used to formulate a system of differential equations to model the response of bone to each treatment. Using Particle Swarm Optimization, optimal treatment regimens are found, including a consideration of budgetary constraints. Additionally, an in vitro model of osteoarthritis in chondrocytes receives RNA silencing of Lrp5. Microarray analysis of gene expression is used to further elucidate the mode of regulation of ADAMTS5, an aggrecanase associated with cartilage degradation, by Lrp5, including the development of a mathematical model. The math model of osteoporosis reveals a quick response to salubrinal and a delayed but substantial response to knee loading. Consideration of cost effectiveness showed that as budgetary constraints increased, treatment did not start until later. The quantitative analysis of ADAMTS5 regulation suggested the involvement of IL1B and p38 MAPK. This research demonstrates the application of quantitative methods to further the usefulness of biomedical and biomolecular research into treatment and signaling pathways. Further work using these techniques can help uncover a bigger picture of osteoarthritis's mode of action and ideal treatment regimens for osteoporosis.

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.

With the newly deployed fifth-generation telecommunications system and upcoming sixth-generation, high-gain antennas with hemispherical scanning capabilities are of high interest. Phased array antennas allow for fast scanning capabilities with electronic beam-steering. In an effort to reduce the number of antenna elements while maintaining the antenna aperture size, the element spacing is increased. However sparse arrays introduce grating lobes in the radiation pattern. An interesting solution to reduce the grating lobes is to integrate a lens with the array. Further, simulating the radiation pattern with a ray-tracing algorithm and the geometrical optics approximation makes for fast simulation times. The presented ray-tracing algorithm in this work speeds up the simulation by 43 times compared to a two-dimensional full-wave simulation. To model the full radiation pattern the rays are shot out from a single point across a set angular space. To emulate an element pattern the rays are excited with a set amplitude distribution. Here, two different methods of obtaining the amplitude are presented and compared to a two-dimensional full-wave COMSOL model. The lens is made from a dielectric, constructed from the conics equation with applied conformal matching layers. The ray path and phase distribution are calculated with Snell's law, the amplitude distribution at the lens aperture is calculated through the ray tube theory, and the radiation pattern with the Kirchhoff Diffraction formula. To optimize the lens shape and an array offset, the ray-tracing algorithm is coupled with a Particle Swarm Optimization algorithm. Two different arrays are used in this thesis, the first constructed from open-ended waveguides and the second using sub-arrays of the same waveguides. The optimized lens for the first array shows that a grating lobe suppression between 1.1-2.0 dB is achievable with a main lobe reduction between 0.2-0.3 dB for scanning to -20 degrees. For the array with sub-arrays, the main lobe suppression is between 0.3-0.9 dB, with a grating lobe suppression of up to 4.0 dB. / Med det nyligen lanserade femte generationens telekommunikationssystem och den kommande sjätte generationen är högförstärkningsantenner med halvsfäriska skanningsmöjligheter av stort intresse. Fasade array-antenner möjliggör snabb skanningskapacitet med elektronisk strålstyrning. I ett försök att minska antalet antennelement samtidigt som antennöppningens storlek bibehålls, ökas elementavståndet. Men glesa arrayer introducerar gallerlober i strålningsmönstret. En intressant lösning för att minska gallerloberna är att integrera en lins med arrayen. Vidare, simulering av strålningsmönstret med en strålspårningsalgoritm och den geometriska optiska approximationen ger snabba simuleringstider. Den presenterade strålspårningsalgoritmen i detta arbete snabbar upp simuleringen med 43 gånger jämfört med en tvådimensionell helvågssimulering. För att modellera hela strålningsmönstret skjuts strålarna ut från en enda punkt över ett fast vinkelutrymme. För att efterlikna ett elementmönster exciteras strålarna med en inställd amplitudfördelning. Här presenteras två olika metoder för att erhålla amplituden och jämförs med en tvådimensionell fullvågs-COMSOL-modell. Linsen är gjord av ett dielektrika konstruerat från koniska ekvationen med applicerade konforma matchande lager. Strålvägen och fasfördelningen beräknas med Snell-lagen, amplitudfördelningen vid linsöppningen beräknas genom strålrörsteorin och strålningsmönstret med Kirchhoff-diffraktionsformeln. För att optimera linsformen och en arrayförskjutning är strålspårningsalgoritmen kopplad med en Particle Swarm algoritm. Två olika arrayer används i denna avhandling, den första konstruerad av vågledare med öppen ände och den andra med hjälp av sub-arrayer av samma vågledare. Den optimerade linsen för den första arrayen visar att en gallerlobsundertryckning mellan 1,1-2,0 dB kan uppnås med en huvudlobsreduktion mellan 0,2-0,3 dB för skanning till -20 grader. För arrayen med sub-arrayer är undertryckningen av huvudloben mellan 0,3-0,9 dB, med en gallerlobundertryckning på upp till 4,0 dB.

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<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)

<p dir="ltr">Electric aircraft offer a promising avenue for reducing aviation's environmental impact through decreased greenhouse gas emissions and noise pollution. Nonetheless, their adoption is hindered by the challenge of limited operational range. Addressed in the study is the range limitation by integrating and optimizing multiple energy storage components—hydrogen fuel cells, Li-ion batteries, and ultracapacitors—through advanced energy management strategies. Utilizing meta-heuristic optimization methods, the research assessed the dynamic performance of each energy component and the effectiveness of the energy management strategy, primarily measured by the hydrogen consumption rate. MATLAB simulations validated the proposed approach, indicating a decrease in hydrogen usage, thus enhancing efficiency and potential cost savings. Artificial Gorilla Troop Optimization yielded the best results with the lowest average hydrogen consumption rate (102.62 grams), outperforming Particle Swarm Optimization (104.68 grams) and Ant Colony Optimization (105.96 grams). The findings suggested that employing a combined energy storage and optimization strategy can significantly improve the operational efficiency and energy conservation of electric aircraft. The study highlighted the potential of such strategies to extend the range of electric aircraft, contributing to a more sustainable aviation future.</p>