In view of the problem of uneven load distribution and energy consumption among nodes in a multi-hop wireless sensor network, this research constructs the routing schedule problem as a MOP (Multi-objective Optimization Problem), and proposed an energy-aware routing optimization scheme RDSEGA based on multi-objective optimization. In this scheme, in order to avoid the searching space explosion problem caused by the increase of nodes, KSP Yen's algorithm was applied to prune the searching space, and the candidate paths selected after pruning are recoded based on priority. Then adopted the improved strengthen elitist genetic algorithm to get the entire network routing optimization scheme with the best energy efficiency. At the same time, in view of the problem of routing discontinuity in the process of path crossover and mutation, new crossover and mutation method was proposed that based on the gene fragments connected by the adjacent node or the same node to maximize the effectiveness of the evolution result. The experimental results prove that the scheme reduced the energy consumption of nodes in the network, the load between nodes becomes more balanced, and the working time of the network has been prolonged nearly 40% after the optimization. This brings convenience to practical applications, especially for those that are inconvenient to replace nodes.

Práce se zabývá popisem nového stochastického vícekriteriálního optimalizačního algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, že algoritmus je schopen řešit nejrůznější typy optimalizačních úloh (s jakýmkoli počtem kritérií, s i bez omezujících podmínek, se spojitým i diskrétním stavovým prostorem). Výsledky algoritmu jsou srovnány s dalšími běžně používanými metodami pro vícekriteriální optimalizaci na velké sadě testovacích úloh. Uvedli jsme novou techniku pro výpočet metriky rozprostření (spread) založené na hledání minimální kostry grafu (Minimum Spanning Tree) pro problémy mající více než dvě kritéria. Doporučené hodnoty pro parametry řídící běh algoritmu byly určeny na základě výsledků jejich citlivostní analýzy. Algoritmus MOSOMA je dále úspěšně použit pro řešení různých návrhových úloh z oblasti elektromagnetismu (návrh Yagi-Uda antény a dielektrických filtrů, adaptivní řízení vyzařovaného svazku v časové oblasti…).

Conceptual design optimization (CDO) is a technique proposed for the structured evaluation of different design concepts. Design grammars provide a flexible modular modelling architecture. The model is generated by a compiler based on predefined components and rules. The rules describe the composition of the model; thus, different models can be optimized by the CDO in one run. This allows considering a mission design including the mission analysis and the system design. The combination of a CDO approach with a model based on design grammars is shown for the concept study of a near-Earth asteroid mission. The mission objective is to investigate two asteroids of different kinds. The CDO reveals that a mission concept using two identical spacecrafts flying to one target each is better than a mission concept with one spacecraft flying to two asteroids consecutively.

The aim and objective of this research is to formulate and solve a multi-objective optimization problem for the optimal placement of multiple rows and multiple columns of fixed flat-plate solar collectors in a field. This is to maximize energy collected from the solar collectors and minimize the investment in terms of the field and collector cost. The resulting multi-objective optimization problem will be solved using genetic algorithm techniques. It is necessary to consider multiple columns of collectors as this can result in obtaining higher amounts of energy from these collectors when costs and maintenance or replacement of damaged parts are concerned. The formulation of such a problem is dependent on several factors, which include shading of collectors, inclination of collectors, distance between the collectors, latitude of location and the global solar radiation (direct beam and diffuse components). This leads to a multi-objective optimization problem. These kind of problems arise often in nature and can be difficult to solve. However the use of evolutionary algorithm techniques has proven effective in solving these kind of problems. Optimizing the distance between the collector rows, the distance between the collector columns and the collector inclination angle, can increase the amount of energy collected from a field of solar collectors thereby maximizing profit and improving return on investment. In this research, the multi-objective optimization problem is solved using two optimization approaches based on genetic algorithms. The first approach is the weighted sum approach where the multi-objective problem is simplified into a single objective optimization problem while the second approach is finding the Pareto front. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Due to an increasing awareness of the importance of sustainable energy use, multi-objective optimization problems for upper-level energy systems are continually being developed and improved. This paper focuses on the modeling and optimization of the Helsinki district heating system and establishing the basis for modeling the Finnish power network. The optimization of the district heating system is conducted for a twenty four hour winter demand period. Partial load behavior of the generators is included by introducing non-linear functions for costs, emissions, and the exergetic efficiency. A fuel cost sensitivity analysis is conducted on the system by considering ten combinations of fuel costs based on high, medium, and low prices for each fuel. The solution sets, called Pareto fronts, are evaluated by post-processing techniques in order to determine the best solution from the optimal set. Because units between some of objective functions are non-commensurable, objective values are normalized and weighted. The results indicate that for today\'s fuel prices the best solution includes a dominating usage of natural gas technologies, while if the price of natural gas is higher than other fuels, natural gas technologies are often not included in the best solution. All of the necessary costs, emissions, and operating information is provided for the the Finnish power network in order to employ a multi-objective optimization on the system. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / An increase in the awareness of environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with the aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play a vital role in improving the efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed, and SOC trajectory, as 50 element time-variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for a given drive cycle. Observations of the trade-off are discussed in the case of Multi-Objective Optimizations.

The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. Algorithmic trading in Financial markets have received a lot of attention in recent years. However, only few literature have explored the applicability and performance of various dynamic multi-objective algorithms (DMOAs) in the Forex market. This dissertation proposes a dynamic multi-swarm multi-objective particle swarm optimization (DMS-MOPSO) to solve dynamic MOPs (DMOPs). In order to explore the performance and applicability of DMS-MOPSO, the algorithm is adapted for the Forex market. This dissertation also explores the performance of di erent variants of dynamic particle swarm optimization (PSO), namely the charge PSO (cPSO) and quantum PSO (qPSO), for the Forex market. However, since the Forex market is not only dynamic but have di erent con icting objectives, a single-objective optimization algorithm (SOA) might not yield pro t over time. For this reason, the Forex market was de ned as a multi-objective optimization problem (MOP). Moreover, maximizing pro t in a nancial time series, like Forex, with computational intelligence (CI) techniques is very challenging. It is even more challenging to make a decision from the solutions of a MOP, like automated Forex trading. This dissertation also explores the e ects of ve decision models (DMs) on DMS-MOPSO and other three state-of-the-art DMOAs, namely the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm, the multi-objective particle swarm optimization algorithm with crowded distance (MOPSOCD) and dynamic non-dominated sorting genetic algorithm II (DNSGA-II). The e ects of constraints handling and the, knowledge sharing approach amongst sub-swarms were explored for DMS-MOPSO. DMS-MOPSO is compared against other state-of-the-art multi-objective algorithms (MOAs) and dynamic SOAs. A sliding window mechanism is employed over di erent types of currency pairs. The focus of this dissertation is to optimized technical indicators to maximized the pro t and minimize the transaction cost. The obtained results showed that both dynamic single-objective optimization (SOO) algorithms and dynamic multi-objective optimization (MOO) algorithms performed better than static algorithms on dynamic poroblems. Moreover, the results also showed that a multi-swarm approach for MOO can solve dynamic MOPs. / Dissertation (MEng)--University of Pretoria, 2019. / Computer Science / MSc / Unrestricted

<p>  </p> <p>Present barriers to electric vehicle (EV) adoption include cost and range anxiety. Dynamic wireless power transfer (DWPT) systems, which send energy from an in-road transmitter to a vehicle in motion, offer potential remedies to both issues. Specifically, they reduce the size and charging needs of the relatively expensive battery system by supplying the power required for vehicle motion and operation. Recently, Purdue researchers have been exploring the development of inductive DWPT systems for Class 8 and 9 trucks operating at highway speeds. This research has included the design of transmitter/receiver coils as well as compensation circuits and power electronics that are required to efficiently transmit 200 kW-level power across a large air gap.</p> <p>In this thesis, a focus is on the derivation of electromagnetic and thermal models that are used to support the design and validation of DWPT systems. Specifically, electromagnetic models have been derived to predict the volume and loss of ferrite-based AC inductors and film capacitor used in compensation circuits. A thermal equivalent circuit of the transmitter has been derived to predict the expected coil and pavement temperatures in DWPT systems that utilize either single- or three-phase transmitter topologies. A description of these models, along with their validation using finite element-based simulation and their use in multi-objective optimization of DWPT systems is provided.</p>

In this dissertation, we develop a new framework for 3D trajectory planning of Autonomous Underwater Vehicles (AUVs) in realistic ocean scenarios. The work is divided into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. We apply a Non-Linear Programming (NLP) to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. To assess the effectiveness of the extended framework, we initially test the methodology in idealized settings. The scheme is then demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we account for uncertainty in the ocean current field, is described by an ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. We formulate stochastic problems that aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to explore various objectives, ranging seamlessly from risk-neutral to risk-averse. The capabilities of the approach are demonstrated using steady and transient currents. Advanced visualization tools have been further designed to simulate results.

Indiana University-Purdue University Indianapolis (IUPUI) / Design optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.