A study of low-energy transfer orbits to the Moon: towards an operational optimization technique

In the Earth-Moon system, low-energy orbits are transfer trajectories from the earth to a circumlunar orbit that require less propellant consumption when compared to the traditional methods. In this work we use a Monte Carlo approach to study a great number of such transfer orbits over a wide range of initial conditions. We make statistical and operational considerations on the resulting data, leading to the description of a reliable way of finding "optimal" mission orbits with the tools of multi-objective optimization

Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants

pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.S

Mixed Heuristic and Mathematical Programming Using Reference Points for Dynamic Data Types Optimization in Multimedia Embedded Systems

New multimedia embedded applications are becoming increasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and efficiently use the limited physical resources of embedded devices. However, the optimization of the DDTs for each target embedded system is a very time-consuming process due to the large design space of possible DDTs implementations and selection for the memory hierarchy of each specific embedded device. Thus, new suitable exploration methods for embedded design metrics (memory accesses, usage and power consumption) need to be developed. In this paper we analyze the benefits of two different exploration techniques for DDTs optimization: Multi-Objective Particle Swarm Optimization (MOPSO) and a Mixed Integer Linear Program (MILP). Furthermore, we propose a novel MOPSO exploration method, OMOPSO*, which uses MILP solutions, as reference points, to guide a MOPSO exploration and reach solutions closer to the real Pareto front of solutions. Our experiments with two real-life embedded applications show that our algorithm achieves 40% better coverage and set of solutions than state-of-the-art optimization methods for DDTs (MOGAs and other MOPSOs)

Neizrazita strategija optimizacije potrošnje energije za paralelno hibridno električno vozilo korištenjem kaotičnog nedominirajućeg genetskog algoritma sortiranja

This paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, and NOx emissions. The main target of this algorithm is to escape from local optima and obtain high quality trade-off solutions. Chaotic initialization operator, chaotic crossover and mutation operators, chaotic disturbance operator, and chaotic local search operator were integrated into non-dominated sorting genetic algorithm II (NSGA-II) to form this new algorithm named chaotic NSGA-II (C-NSGA-II). Simulation results and comparisons demonstrated that chaotic operators can enhance searching ability for optimal solutions. In conclusion, C-NSGA-II is suitable for solving HEV energy management optimization problem.Ovaj rad prikazuje paralelno hibridno električno vozilo (HEV) opremljeno hibridnim spremnikom energije. Kako bi se omogućila funkcionalnost pogonskog sklopa ovakvog HEV-a korištena je strategija raspolaganja energijom zasnovana na neizrazitoj logici. Također, prikazan je više kriterijski genetski algoritam kaosa za optimiranje parametara neizrazite funkcije povezanih s ekonomskim pokazateljem te pokazateljima emisije HC-a, CO-a i NOx-a. Osnovni cilj algoritma je omogućiti izlazak iz lokalnih optimuma i uspostavljanjem kompromisa omogućiti dosezanje boljih rješenja. Kaotični inicijalizacijski operator, kaotično križanje i operator mutacije, kaotični operator poremećaja i kaotični operator lokalnog pretraživanje uključeni su u nedominirajući genetski algoritam sortiranja II (NSGA-II) u svrhu formulacije novog problema nazvanog kaotični NSGA-II (C-NSGA-II). Simulacijski rezultati i usporedbe prikazuju kako kaotični operator može povećati uspješnost traženja optimalnog rješenja. Zaključno, C-NSGA-II je primjeren za rješavanje problema raspolaganja energijom u HEV-u

Developing collaborative planning support tools for optimised farming in Western Australia

Land-use (farm) planning is a highly complex and dynamic process. A land-use plan can be optimal at one point in time, but its currency can change quickly due to the dynamic nature of the variables driving the land-use decision-making process. These include external drivers such as weather and produce markets, that also interact with the biophysical interactions and management activities of crop production.The active environment of an annual farm planning process can be envisioned as being cone-like. At the beginning of the sowing year, the number of options open to the manager is huge, although uncertainty is high due to the inability to foresee future weather and market conditions. As the production year reveals itself, the uncertainties around weather and markets become more certain, as does the impact of weather and management activities on future production levels. This restricts the number of alternative management options available to the farm manager. Moreover, every decision made, such as crop type sown in a paddock, will constrains the range of management activities possible in that paddock for the rest of the growing season.This research has developed a prototype Land-use Decision Support System (LUDSS) to aid farm managers in their tactical farm management decision making. The prototype applies an innovative approach that mimics the way in which a farm manager and/or consultant would search for optimal solutions at a whole-farm level. This model captured the range of possible management activities available to the manager and the impact that both external (to the farm) and internal drivers have on crop production and the environment. It also captured the risk and uncertainty found in the decision space.The developed prototype is based on a Multiple Objective Decision-making (MODM) - á Posteriori approach incorporating an Exhaustive Search method. The objective set used for the model is: maximising profit and minimising environmental impact. Pareto optimisation theory was chosen as the method to select the optimal solution and a Monte Carlo simulator is integrated into the prototype to incorporate the dynamic nature of the farm decision making process. The prototype has a user-friendly front and back end to allow farmers to input data, drive the application and extract information easily

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses

Sensitivity analysis and evolutionary optimization for building design

In order to achieve global carbon reduction targets, buildings must be designed to be energy efficient. Building performance simulation methods, together with sensitivity analysis and evolutionary optimization methods, can be used to generate design solution and performance information that can be used in identifying energy and cost efficient design solutions. Sensitivity analysis is used to identify the design variables that have the greatest impacts on the design objectives and constraints. Multi-objective evolutionary optimization is used to find a Pareto set of design solutions that optimize the conflicting design objectives while satisfying the design constraints; building design being an inherently multi-objective process. For instance, there is commonly a desire to minimise both the building energy demand and capital cost while maintaining thermal comfort. Sensitivity analysis has previously been coupled with a model-based optimization in order to reduce the computational effort of running a robust optimization and in order to provide an insight into the solution sensitivities in the neighbourhood of each optimum solution. However, there has been little research conducted to explore the extent to which the solutions found from a building design optimization can be used for a global or local sensitivity analysis, or the extent to which the local sensitivities differ from the global sensitivities. It has also been common for the sensitivity analysis to be conducted using continuous variables, whereas building optimization problems are more typically formulated using a mixture of discretized-continuous variables (with physical meaning) and categorical variables (without physical meaning). This thesis investigates three main questions; the form of global sensitivity analysis most appropriate for use with problems having mixed discretised-continuous and categorical variables; the extent to which samples taken from an optimization run can be used in a global sensitivity analysis, the optimization process causing these solutions to be biased; and the extent to which global and local sensitivities are different. The experiments conducted in this research are based on the mid-floor of a commercial office building having 5 zones, and which is located in Birmingham, UK. The optimization and sensitivity analysis problems are formulated with 16 design variables, including orientation, heating and cooling setpoints, window-to-wall ratios, start and stop time, and construction types. The design objectives are the minimisation of both energy demand and capital cost, with solution infeasibility being a function of occupant thermal comfort. It is concluded that a robust global sensitivity analysis can be achieved using stepwise regression with the use of bidirectional elimination, rank transformation of the variables and BIC (Bayesian information criterion). It is concluded that, when the optimization is based on a genetic algorithm, that solutions taken from the start of the optimization process can be reliably used in a global sensitivity analysis, and therefore, there is no need to generate a separate set of random samples for use in the sensitivity analysis. The extent to which the convergence of the variables during the optimization can be used as a proxy for the variable sensitivities has also been investigated. It is concluded that it is not possible to identify the relative importance of variables through the optimization, even though the most important variable exhibited fast and stable convergence. Finally, it is concluded that differences exist in the variable rankings resulting from the global and local sensitivity methods, although the top-ranked solutions from each approach tend to be the same. It also concluded that the sensitivity of the objectives and constraints to all variables is obtainable through a local sensitivity analysis, but that a global sensitivity analysis is only likely to identify the most important variables. The repeatability of these conclusions has been investigated and confirmed by applying the methods to the example design problem with the building being located in four different climates (Birmingham, UK; San Francisco, US; and Chicago, US)

Delay-dependent output feedback compensators for a class of networked control systems

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2014Sistemas de controle via rede (NCS, do inglês Networked Control Systems) são uma classe especial de sistemas amostrados digitalmente, nos quais os dispositivos do sistema de controle se comunicam através de uma rede de comunicação (como mostrado na Fig. I). Significantes avanços tecnológicos tem levado a um maior interesse tanto na utilização de NCS em ambiente industrial (MOYNE; TILBURY, 2007), quanto em pesquisas relacionadas ao assunto (HESPANHA; NAGHSHTABRIZI; XU,2007; HEEMELS; WOUW, 2010; ZHANG; GAO; KAYNAK, 2013). Algumas das vantagens oferecidas por tais sistemas, com relação a sistemas de controle tradicionais, compreendem menor custo de implementação, flexibilidade e facilidade de manutenção. Apesar disso, inerentemente alguns efeitos indesejados também podem ocorrer, tais como atrasos na comunicação e intervalos de amostragem variantes, ocasionando degradação no desempenho do sistema em malha fechada. Devido a esses efeitos, a análise de estabilidade e também o projeto de controladores para NCS tornam-se mais desafiadores (TANG; YU, 2007). De modo geral, os estudos sobre NCS podem ser divididos em duas grandes áreas: controle da rede e controle via rede (GUPTA; CHOW, 2010). A primeira está mais interessada em proporcionar uma melhor qualidade no serviço de transmissão de dados realizado pela rede de comunicação, enquanto a segunda objetiva uma melhor qualidade do desempenho dos sistemas de controle sob determinadas condições induzidas pelos efeitos da utilização da rede. Embora tipicamente tratadas de forma separada, recentemente alguns esforços têm sido empreendidos de modo a integrar algumas características de ambas as áreas em fase de projeto, as chamadas estratégias de co-design (TORNGREN et al., 2006). Uma abordagem integrada é necessária de modo a se obter uma maior compreensão do funcionamento de um NCS, podendo assim obter um melhor desempenho geral do sistema. Neste contexto, especialmente levando em consideração que o uso rede de comunicação é limitado, tal recurso deve ser corretamente distribuído entre os sistemas de controle de modo a garantir um funcionamento adequado. Além disso, requisitos de desempenho individuais de cada planta também devem ser cumpridos, mesmo sujeitos a tais restrições de limites de recursos.Abstract: Networked control system (NCS) is a special class of sampled-data system where control systems devices are interconnected through a communication network. Despite the advantages, such as lower cost, flexibility and easy of maintenance compared to a more traditional implementation, some undesired effects may be induced by the use of a shared medium in the feedback loop, for instance, time-varying sampling intervals and delays. Due to the multidisciplinary nature of an NCS, the analysis and design of such systems also demand a more comprehensive approach. Thus, the main objective of this thesis is to propose some strategies for the synthesis of dynamic output feedback compensators, assuming an industrial network control system environment with temporal behavior features and requirements. Throughout this document, the NCS is modeled considering unknown time-varying delays, which leads to an uncertain system representation, later overapproximated by a convex polytope with additional norm-bounded uncertainty. Based on parameter dependent Lyapunov functions, closed-loop stability conditions are provided, which can be verified in terms of feasibility of a set of linear matrix inequalities (LMIs). The control designs are then promptly derived from the stability conditions, leading to delay-dependent compensators. Furthermore, an integrated control design and resource management strategy is proposed, taking into account the controller design while also addressing the shared nature of the communication network. This co-design strategy assumes that a supervisor task has the knowledge of all devices that access the network, as well as their allocated bandwidths. Numerical examples and simulations are provided to illustrate the effectiveness of the proposed design methodologies