This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

We meet with solving of optimization problems every day, when we try to do our tasks in the best way. An Ant Colony Optimization is an algorithm inspired by behavior of ants seeking a source of food. The Ant Colony Optimization is successfuly using on optimization tasks, on which is not possible to use a classical optimization methods. A Genetic Algorithm is inspired by transmision of a genetic information during crossover. The Genetic Algorithm is used for solving optimization tasks like the ACO algorithm. The result of my master's thesis is created simulator for solving choosen optimization tasks by the ACO algorithm and the Genetic Algorithm and a comparison of gained results on implemented tasks.

Thesis deals with discrete optimization problems. It focusses on faster ways to find good solutions by means of heuristics and parallel processing. Based on ant colony optimization (ACO) algorithm coupled with k-optimization local search approach, it aims at massively parallel computing on graphics processors provided by Nvidia CUDA platform. Well-known travelling salesman problem (TSP) is used as a case study. Solution is based on dividing task into subproblems using tour-based partitioning, parallel processing of distinct parts and their consecutive recombination. Provided parallel code can perform computation more than seventeen times faster than the sequential version.

<div>The innovations in Magnetic Resonance Imaging (MRI) in the recent decades have given rise to large open-source datasets. MRI affords researchers the ability to look at both structure and function of the human brain. This dissertation will make use of one of these large open-source datasets, the Human Connectome Project (HCP), to study the structural and functional connectivity in the brain.</div><div>Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no <i>a priori</i> information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely <i>pheromone</i> and <i>edge perception</i>, to define the cognizance and subsequent behaviour of the ants on the network and the communication processes happening between source and target. Simulations with different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These measurements are tested as individual and combined descriptors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. This framework may be used as a test-bed for different communication regimes on top of an underlying topology.</div><div>The assessment of brain <i>fingerprints</i> has emerged in the recent years as an important tool to study individual differences. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. We extend the concept of brain connectivity fingerprints beyond test/retest and assess <i>fingerprint gradients</i> in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (<i>subject fingerprint</i>), but also between the scans of monozygotic and dizygotic twins (<i>twin fingerprint</i>). We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and time series parcellated according to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and time series for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community.</div><div>Lastly, we have estimated the effect of genetics and environment on the original and optimally reconstructed FC with an ACE model.</div>

Die Bachelorarbeit behandelt die Themen der Selbstorganisation in Produktionssystemen im Kontext von Industrie 4.0. Dabei wird gezeigt, wie man mithilfe von einer Ameisenkolonieoptimierung die Reihenfolgeplanung organisieren kann.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis / The bachelor thesis is about self organization in production systems in the context of Industry 4.0. Its about ant colony optimization for scheduling in the production planning.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis

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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>

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 Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).

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.