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<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>

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.

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This diploma thesis deals with evolutionary algorithms used for travelling salesman problem (TSP). In the first section, there are theoretical foundations of a graph theory and computational complexity theory. Next section contains a description of chosen optimization algorithms. The aim of the diploma thesis is to implement an application that solve TSP using evolutionary algorithms.

Planar antennas are used in several technical applications. The family of planar antennas contains microstrip antennas, which are very popular due to the low weight, low profile, simple manufacturing and easy mass production. Lower gain and excitation of surface waves are disadvantages of microstrip antennas. The propagation of surface waves can be efficiently suppressed if the conventional substrate is replaced by an electromagnetic bandgap (EBG) substrate. Microstrip antennas on EBG substrates have been presented in an open literature for several years. Nevertheless, no published work is devoted to the design of EBG substrates, which can suppress surface waves at several frequencies those cannot be covered by a single bandgap. In order to reach optimum parameters of designed antennas, selected global optimization methods are applied (genetic algorithms, particle swarm optimization, ant colony optimization).

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.

This doctoral thesis is focused on metrics and the criteria for socio-technical system diagnostics, which is a high profile topic for companies wanting to ensure the best in product quality. More and more customers are requiring suppliers to prove reliability in the production and supply quality of products according to given specifications. Consequently the ability to produce quality goods corresponding to customer requirements has become a fundamental condition in order to remain competitive. The thesis firstly lays out the basic strategies and rules which are prerequisite for a successful working company in order to ensure provision of quality goods at competitive costs. Next, methods and tools for planning are discussed. Planning is important in its impact on budget, time schedules, and necessary sourcing quantification. Risk analysis is also included to help define preventative actions, and reduce the probability of error and potential breakdown of the entire company. The next part of the thesis deals with optimisation problems, which are solved by Swarm based optimisation. Algorithms and their utilisation in industry are described, in particular the Vehicle routing problem and Travelling salesman problem, used as tools for solving specialist problems within manufacturing corporations. The final part of the thesis deals with Qualitative modelling, where solutions can be achieved with less exact quantitative information of the surveyed model. The text includes qualitative algebra descriptions, which discern only three possible values – positive, constant and negative, which are sufficient in the demonstration of trends. The results can also be conveniently represented using graph theory tools.

Solid waste management is a global issue that affects everyone. The management of waste collection routes is a critical challenge in urban environments, primarily due to inefficient routing. This thesis investigates the use of real-time virtual replicas, namely Digital Twins to optimize waste collection routes. By leveraging the capabilities of digital twins, this study intends to improve the effectiveness and efficiency of waste collection operations. The ‘gap’ that the study aims to uncover is hence at the intersection of smart cities, Digital Twins, and waste collection routing. The research methodology comprises of three key components. First, an exploration of five widely used metaheuristic algorithms provides a qualitative understanding of their applicability in vehicle routing, and consecutively waste collection route optimization. Building on this foundation, a simple smart routing scenario for waste collection is presented, highlighting the limitations of a purely Internet of Things (IoT)-based approach. Next, the findings from this demonstration motivate the need for a more data-driven and intelligent solution, leading to the introduction of the Digital Twin concept. Subsequently, a twin framework is developed, which encompasses the technical anatomy and methodology required to create and utilize Digital Twins to optimize waste collection, considering factors such as real-time data integration, predictive analytics, and optimization algorithms. The outcome of this research contributes to the growing concept of smart cities and paves the way toward practical implementations in revolutionizing waste management and creating a sustainable future. / Sophantering är ett globalt problem som påverkar alla, och hantering av sophämtningsrutter är en kritisk utmaning i stadsmiljöer. Den här avhandlingen undersöker användningen av virtuella kopior i realtid, nämligen digitala tvillingar, för att optimera sophämtningsrutter. Genom att utnyttja digitala tvillingars förmågor, avser den här studien att förbättra effektiviteten av sophämtning. Forskningsmetoden består av tre nyckeldelar. Först, en undersökning av fem välanvända Metaheuristika algoritmer som ger en kvalitativ förståelse av deras applicerbarhet i fordonsdirigering och således i optimeringen av sophämtningsrutter. Baserat på detta presenteras ett enkelt smart ruttscenario för sophämtning som understryker bristerna av att bara använda Internet of Things (IoT). Sedan motiverar resultaten av demonstrationen nödvändigheten för en mer datadriven och intelligent lösning, vilket leder till introduktionen av konceptet med digitala tvillingar. Därefter utvecklas ett ramverk för digitala tvillingar som omfattar den tekniska anatomin och metod som krävs för att skapa och använda digitala tvillingar för att optimera sophämtningsrutter. Dessa tar i beaktning faktorer såsom realtidsdataintegrering, prediktiv analys och optimeringsalgoritmer. Slutsatserna av studien bidrar till det växande konceptet av smarta städer och banar väg för praktisk implementation i revolutionerande sophantering och för skapandet för en hållbar framtid.

The multiple constant multiplication (MCM) operation is a fundamental operation in digital signal processing (DSP) and digital image processing (DIP). Examples of the MCM are in finite impulse response (FIR) and infinite impulse response (IIR) filters, matrix multiplication, and transforms. The aim of this work is minimizing the complexity of the MCM operation using common subexpression elimination (CSE) technique and redundant number representations. The CSE technique searches and eliminates common digit patterns (subexpressions) among MCM coefficients. More common subexpressions can be found by representing the MCM coefficients using redundant number representations. A CSE algorithm is proposed that works on a type of redundant numbers called the zero-dominant set (ZDS). The ZDS is an extension over the representations of minimum number of non-zero digits called minimum Hamming weight (MHW). Using the ZDS improves CSE algorithms' performance as compared with using the MHW representations. The disadvantage of using the ZDS is it increases the possibility of overlapping patterns (digit collisions). In this case, one or more digits are shared between a number of patterns. Eliminating a pattern results in losing other patterns because of eliminating the common digits. A pattern preservation algorithm (PPA) is developed to resolve the overlapping patterns in the representations. A tree and graph encoders are proposed to generate a larger space of number representations. The algorithms generate redundant representations of a value for a given digit set, radix, and wordlength. The tree encoder is modified to search for common subexpressions simultaneously with generating of the representation tree. A complexity measure is proposed to compare between the subexpressions at each node. The algorithm terminates generating the rest of the representation tree when it finds subexpressions with maximum sharing. This reduces the search space while minimizes the hardware complexity. A combinatoric model of the MCM problem is proposed in this work. The model is obtained by enumerating all the possible solutions of the MCM that resemble a graph called the demand graph. Arc routing on this graph gives the solutions of the MCM problem. A similar arc routing is found in the capacitated arc routing such as the winter salting problem. Ant colony optimization (ACO) meta-heuristics is proposed to traverse the demand graph. The ACO is simulated on a PC using Python programming language. This is to verify the model correctness and the work of the ACO. A parallel simulation of the ACO is carried out on a multi-core super computer using C++ boost graph library.