Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

A tutorial on program development and optimization using the Ciao preprocessor

We present in a tutorial fashion CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and to perform high-level program transformations such as multiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost).

Specialization and optimization of constraint programs with dynamic scheduling

In this report we discuss some of the issues involved in the specialization and optimization of constraint logic programs with dynamic scheduling. Dynamic scheduling, as any other form of concurrency, increases the expressive power of constraint logic programs, but also introduces run-time overhead. The objective of the specialization and optimization is to reduce as much as possible such overhead automatically, while preserving the semantics of the original programs. This is done by program transformation based on global analysis. We present implementation techniques for this purpose and report on experimental results obtained from an implementation of the techniques in the context of the CIAO compiler.

Multiobjective optimization applied to structural sizing of low cost university-class microsatellite projects

In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In this paper, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. Finally, a Genetic Algorithm (GA) multiobjective optimization is applied to the design space. The result is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget, which gives a useful insight to the design team at the early phases of the design.

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Efficient design and optimization of bio-photonic sensing cells (BICELLs) for label free biosensing

In previous works we demonstrated the benefits of using micro–nano patterning materials to be used as bio-photonic sensing cells (BICELLs), referred as micro–nano photonic structures having immobilized bioreceptors on its surface with the capability of recognizing the molecular binding by optical transduction. Gestrinone/anti-gestrinone and BSA/anti-BSA pairs were proven under different optical configurations to experimentally validate the biosensing capability of these bio-sensitive photonic architectures. Moreover, Three-Dimensional Finite Difference Time Domain (FDTD) models were employed for simulating the optical response of these structures. For this article, we have developed an effective analytical simulation methodology capable of simulating complex biophotonic sensing architectures. This simulation method has been tested and compared with previous experimental results and FDTD models. Moreover, this effective simulation methodology can be used for efficiently design and optimize any structure as BICELL. In particular for this article, six different BICELL's types have been optimized. To carry out this optimization we have considered three figures of merit: optical sensitivity, Q-factor and signal amplitude. The final objective of this paper is not only validating a suitable and efficient optical simulation methodology but also demonstrating the capability of this method for analyzing the performance of a given number of BICELLs for label-free biosensing.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Transformation�based implementation and optimization of programs exploiting the basic Andorra model.

The characteristics of CC and CLP systems are in principle very dierent However a recent trend towards convergence in the implementation techniques for these systems can be observed While CLP and Prolog systems have been incorporating capabilities to deal with userdened suspension and coroutining CC compilers have been trying to coalesce negrained tasks into coarsergrained sequential threads This convergence of techniques opens up the possibility of having a general purpose kernel language and abstract machine to serve as a compilation target for a variety of userlevel languages We propose a transformation technique directed towards such an objective In particular we report on techniques to support the Andorra computational model essentially emulating the AndorraI system via program transformation into a sequential language with delay primitives The system is automatic comprising an optional program analyzer and a basic transformer to the kernel language It turns out that a simple parallel CLP or Prolog system with dynamic scheduling is sucient as a kernel language for this purpose The preliminary results are quite encouraging performance of the resulting system is comparable to the current AndorraI implementation.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework

Methods for predicting the shear capacity of FRP shear strengthened RC beams assume the traditional approach of superimposing the contribution of the FRP reinforcing to the contributions from the reinforcing steel and the concrete. These methods become the basis for most guides for the design of externally bonded FRP systems for strengthening concrete structures. The variations among them come from the way they account for the effect of basic shear design parameters on shear capacity. This paper presents a simple method for defining improved equations to calculate the shear capacity of reinforced concrete beams externally shear strengthened with FRP. For the first time, the equations are obtained in a multiobjective optimization framework solved by using genetic algorithms, resulting from considering simultaneously the experimental results of beams with and without FRP external reinforcement. The performance of the new proposed equations is compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. The proposed procedure is also reformulated as a constrained optimization problem to provide more conservative shear predictions.

Sharp imaging of Multiple object pointS: how and why

Is it possible to sharply image M object points with N surfaces when N menor que M? Under what conditions? Why is it interesting for optimization? What is the role of the SMS method?

A framework for the analysis and optimization of delay in multiview video coding schemes

Esta tesis presenta un novedoso marco de referencia para el análisis y optimización del retardo de codificación y descodificación para vídeo multivista. El objetivo de este marco de referencia es proporcionar una metodología sistemática para el análisis del retardo en codificadores y descodificadores multivista y herramientas útiles en el diseño de codificadores/descodificadores para aplicaciones con requisitos de bajo retardo. El marco de referencia propuesto caracteriza primero los elementos que tienen influencia en el comportamiento del retardo: i) la estructura de predicción multivista, ii) el modelo hardware del codificador/descodificador y iii) los tiempos de proceso de cuadro. En segundo lugar, proporciona algoritmos para el cálculo del retardo de codificación/ descodificación de cualquier estructura arbitraria de predicción multivista. El núcleo de este marco de referencia consiste en una metodología para el análisis del retardo de codificación/descodificación multivista que es independiente de la arquitectura hardware del codificador/descodificador, completada con un conjunto de modelos que particularizan este análisis del retardo con las características de la arquitectura hardware del codificador/descodificador. Entre estos modelos, aquellos basados en teoría de grafos adquieren especial relevancia debido a su capacidad de desacoplar la influencia de los diferentes elementos en el comportamiento del retardo en el codificador/ descodificador, mediante una abstracción de su capacidad de proceso. Para revelar las posibles aplicaciones de este marco de referencia, esta tesis presenta algunos ejemplos de su utilización en problemas de diseño que afectan a codificadores y descodificadores multivista. Este escenario de aplicación cubre los siguientes casos: estrategias para el diseño de estructuras de predicción que tengan en consideración requisitos de retardo además del comportamiento tasa-distorsión; diseño del número de procesadores y análisis de los requisitos de velocidad de proceso en codificadores/ descodificadores multivista dado un retardo objetivo; y el análisis comparativo del comportamiento del retardo en codificadores multivista con diferentes capacidades de proceso e implementaciones hardware. ABSTRACT This thesis presents a novel framework for the analysis and optimization of the encoding and decoding delay for multiview video. The objective of this framework is to provide a systematic methodology for the analysis of the delay in multiview encoders and decoders and useful tools in the design of multiview encoders/decoders for applications with low delay requirements. The proposed framework characterizes firstly the elements that have an influence in the delay performance: i) the multiview prediction structure ii) the hardware model of the encoder/decoder and iii) frame processing times. Secondly, it provides algorithms for the computation of the encoding/decoding delay of any arbitrary multiview prediction structure. The core of this framework consists in a methodology for the analysis of the multiview encoding/decoding delay that is independent of the hardware architecture of the encoder/decoder, which is completed with a set of models that particularize this delay analysis with the characteristics of the hardware architecture of the encoder/decoder. Among these models, the ones based in graph theory acquire special relevance due to their capacity to detach the influence of the different elements in the delay performance of the encoder/decoder, by means of an abstraction of its processing capacity. To reveal possible applications of this framework, this thesis presents some examples of its utilization in design problems that affect multiview encoders and decoders. This application scenario covers the following cases: strategies for the design of prediction structures that take into consideration delay requirements in addition to the rate-distortion performance; design of number of processors and analysis of processor speed requirements in multiview encoders/decoders given a target delay; and comparative analysis of the encoding delay performance of multiview encoders with different processing capabilities and hardware implementations.

Multidisciplinary Design Optimization Application to Conceptual Design of University-class Microsatellite Projects

Esta tesis se ha realizado en el contexto del proyecto UPMSat-2, que es un microsatélite diseñado, construido y operado por el Instituto Universitario de Microgravedad "Ignacio Da Riva" (IDR / UPM) de la Universidad Politécnica de Madrid. Aplicación de la metodología Ingeniería Concurrente (Concurrent Engineering: CE) en el marco de la aplicación de diseño multidisciplinar (Multidisciplinary Design Optimization: MDO) es uno de los principales objetivos del presente trabajo. En los últimos años, ha habido un interés continuo en la participación de los grupos de investigación de las universidades en los estudios de la tecnología espacial a través de sus propios microsatélites. La participación en este tipo de proyectos tiene algunos desafíos inherentes, tales como presupuestos y servicios limitados. Además, debido al hecho de que el objetivo principal de estos proyectos es fundamentalmente educativo, por lo general hay incertidumbres en cuanto a su misión en órbita y cargas útiles en las primeras fases del proyecto. Por otro lado, existen limitaciones predeterminadas para sus presupuestos de masa, volumen y energía, debido al hecho de que la mayoría de ellos están considerados como una carga útil auxiliar para el lanzamiento. De este modo, el costo de lanzamiento se reduce considerablemente. En este contexto, el subsistema estructural del satélite es uno de los más afectados por las restricciones que impone el lanzador. Esto puede afectar a diferentes aspectos, incluyendo las dimensiones, la resistencia y los requisitos de frecuencia. En la primera parte de esta tesis, la atención se centra en el desarrollo de una herramienta de diseño del subsistema estructural que evalúa, no sólo las propiedades de la estructura primaria como variables, sino también algunas variables de nivel de sistema del satélite, como la masa de la carga útil y la masa y las dimensiones extremas de satélite. Este enfoque permite que el equipo de diseño obtenga una mejor visión del diseño en un espacio de diseño extendido. La herramienta de diseño estructural se basa en las fórmulas y los supuestos apropiados, incluyendo los modelos estáticos y dinámicos del satélite. Un algoritmo genético (Genetic Algorithm: GA) se aplica al espacio de diseño para optimizaciones de objetivo único y también multiobjetivo. El resultado de la optimización multiobjetivo es un Pareto-optimal basado en dos objetivo, la masa total de satélites mínimo y el máximo presupuesto de masa de carga útil. Por otro lado, la aplicación de los microsatélites en misiones espaciales es de interés por su menor coste y tiempo de desarrollo. La gran necesidad de las aplicaciones de teledetección es un fuerte impulsor de su popularidad en este tipo de misiones espaciales. Las misiones de tele-observación por satélite son esenciales para la investigación de los recursos de la tierra y el medio ambiente. En estas misiones existen interrelaciones estrechas entre diferentes requisitos como la altitud orbital, tiempo de revisita, el ciclo de vida y la resolución. Además, todos estos requisitos puede afectar a toda las características de diseño. Durante los últimos años la aplicación de CE en las misiones espaciales ha demostrado una gran ventaja para llegar al diseño óptimo, teniendo en cuenta tanto el rendimiento y el costo del proyecto. Un ejemplo bien conocido de la aplicación de CE es la CDF (Facilidad Diseño Concurrente) de la ESA (Agencia Espacial Europea). Está claro que para los proyectos de microsatélites universitarios tener o desarrollar una instalación de este tipo parece estar más allá de las capacidades del proyecto. Sin embargo, la práctica de la CE a cualquier escala puede ser beneficiosa para los microsatélites universitarios también. En la segunda parte de esta tesis, la atención se centra en el desarrollo de una estructura de optimización de diseño multidisciplinar (Multidisciplinary Design Optimization: MDO) aplicable a la fase de diseño conceptual de microsatélites de teledetección. Este enfoque permite que el equipo de diseño conozca la interacción entre las diferentes variables de diseño. El esquema MDO presentado no sólo incluye variables de nivel de sistema, tales como la masa total del satélite y la potencia total, sino también los requisitos de la misión como la resolución y tiempo de revisita. El proceso de diseño de microsatélites se divide en tres disciplinas; a) diseño de órbita, b) diseño de carga útil y c) diseño de plataforma. En primer lugar, se calculan diferentes parámetros de misión para un rango práctico de órbitas helio-síncronas (sun-synchronous orbits: SS-Os). Luego, según los parámetros orbitales y los datos de un instrumento como referencia, se calcula la masa y la potencia de la carga útil. El diseño de la plataforma del satélite se estima a partir de los datos de la masa y potencia de los diferentes subsistemas utilizando relaciones empíricas de diseño. El diseño del subsistema de potencia se realiza teniendo en cuenta variables de diseño más detalladas, como el escenario de la misión y diferentes tipos de células solares y baterías. El escenario se selecciona, de modo de obtener una banda de cobertura sobre la superficie terrestre paralelo al Ecuador después de cada intervalo de revisita. Con el objetivo de evaluar las interrelaciones entre las diferentes variables en el espacio de diseño, todas las disciplinas de diseño mencionados se combinan en un código unificado. Por último, una forma básica de MDO se ajusta a la herramienta de diseño de sistema de satélite. La optimización del diseño se realiza por medio de un GA con el único objetivo de minimizar la masa total de microsatélite. Según los resultados obtenidos de la aplicación del MDO, existen diferentes puntos de diseños óptimos, pero con diferentes variables de misión. Este análisis demuestra la aplicabilidad de MDO para los estudios de ingeniería de sistema en la fase de diseño conceptual en este tipo de proyectos. La principal conclusión de esta tesis, es que el diseño clásico de los satélites que por lo general comienza con la definición de la misión y la carga útil no es necesariamente la mejor metodología para todos los proyectos de satélites. Un microsatélite universitario, es un ejemplo de este tipo de proyectos. Por eso, se han desarrollado un conjunto de herramientas de diseño para encarar los estudios de la fase inicial de diseño. Este conjunto de herramientas incluye diferentes disciplinas de diseño centrados en el subsistema estructural y teniendo en cuenta una carga útil desconocida a priori. Los resultados demuestran que la mínima masa total del satélite y la máxima masa disponible para una carga útil desconocida a priori, son objetivos conflictivos. En este contexto para encontrar un Pareto-optimal se ha aplicado una optimización multiobjetivo. Según los resultados se concluye que la selección de la masa total por satélite en el rango de 40-60 kg puede considerarse como óptima para un proyecto de microsatélites universitario con carga útil desconocida a priori. También la metodología CE se ha aplicado al proceso de diseño conceptual de microsatélites de teledetección. Los resultados de la aplicación del CE proporcionan una clara comprensión de la interacción entre los requisitos de diseño de sistemas de satélites, tales como la masa total del microsatélite y la potencia y los requisitos de la misión como la resolución y el tiempo de revisita. La aplicación de MDO se hace con la minimización de la masa total de microsatélite. Los resultados de la aplicación de MDO aclaran la relación clara entre los diferentes requisitos de diseño del sistema y de misión, así como que permiten seleccionar las líneas de base para el diseño óptimo con el objetivo seleccionado en las primeras fase de diseño. ABSTRACT This thesis is done in the context of UPMSat-2 project, which is a microsatellite under design and manufacturing at the Instituto Universitario de Microgravedad “Ignacio Da Riva” (IDR/UPM) of the Universidad Politécnica de Madrid. Application of Concurrent Engineering (CE) methodology in the framework of Multidisciplinary Design application (MDO) is one of the main objectives of the present work. In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In the first part of this thesis, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the satellite system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on the analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. A Genetic Algorithm (GA) is applied to the design space for both single and multiobejective optimizations. The result of the multiobjective optimization is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget. On the other hand, the application of the microsatellites is of interest for their less cost and response time. The high need for the remote sensing applications is a strong driver of their popularity in space missions. The satellite remote sensing missions are essential for long term research around the condition of the earth resources and environment. In remote sensing missions there are tight interrelations between different requirements such as orbital altitude, revisit time, mission cycle life and spatial resolution. Also, all of these requirements can affect the whole design characteristics. During the last years application of the CE in the space missions has demonstrated a great advantage to reach the optimum design base lines considering both the performance and the cost of the project. A well-known example of CE application is ESA (European Space Agency) CDF (Concurrent Design Facility). It is clear that for the university-class microsatellite projects having or developing such a facility seems beyond the project capabilities. Nevertheless practicing CE at any scale can be beneficiary for the university-class microsatellite projects. In the second part of this thesis, the main focus is on developing a MDO framework applicable to the conceptual design phase of the remote sensing microsatellites. This approach enables the design team to evaluate the interaction between the different system design variables. The presented MDO framework contains not only the system level variables such as the satellite total mass and total power, but also the mission requirements like the spatial resolution and the revisit time. The microsatellite sizing process is divided into the three major design disciplines; a) orbit design, b) payload sizing and c) bus sizing. First, different mission parameters for a practical range of sun-synchronous orbits (SS-Os) are calculated. Then, according to the orbital parameters and a reference remote sensing instrument, mass and power of the payload are calculated. Satellite bus sizing is done based on mass and power calculation of the different subsystems using design estimation relationships. In the satellite bus sizing, the power subsystem design is realized by considering more detailed design variables including a mission scenario and different types of solar cells and batteries. The mission scenario is selected in order to obtain a coverage belt on the earth surface parallel to the earth equatorial after each revisit time. In order to evaluate the interrelations between the different variables inside the design space all the mentioned design disciplines are combined in a unified code. The integrated satellite system sizing tool developed in this section is considered as an application of the CE to the conceptual design of the remote sensing microsatellite projects. Finally, in order to apply the MDO methodology to the design problem, a basic MDO framework is adjusted to the developed satellite system design tool. Design optimization is done by means of a GA single objective algorithm with the objective function as minimizing the microsatellite total mass. According to the results of MDO application, there exist different optimum design points all with the minimum satellite total mass but with different mission variables. This output demonstrates the successful applicability of MDO approach for system engineering trade-off studies at the conceptual design phase of the design in such projects. The main conclusion of this thesis is that the classical design approach for the satellite design which usually starts with the mission and payload definition is not necessarily the best approach for all of the satellite projects. The university-class microsatellite is an example for such projects. Due to this fact an integrated satellite sizing tool including different design disciplines focusing on the structural subsystem and considering unknown payload is developed. According to the results the satellite total mass and available mass for the unknown payload are conflictive objectives. In order to find the Pareto-optimal a multiobjective GA optimization is conducted. Based on the optimization results it is concluded that selecting the satellite total mass in the range of 40-60 kg can be considered as an optimum approach for a university-class microsatellite project with unknown payload(s). Also, the CE methodology is applied to the remote sensing microsatellites conceptual design process. The results of CE application provide a clear understanding of the interaction between satellite system design requirements such as satellite total mass and power and the satellite mission variables such as revisit time and spatial resolution. The MDO application is done with the total mass minimization of a remote sensing satellite. The results from the MDO application clarify the unclear relationship between different system and mission design variables as well as the optimum design base lines according to the selected objective during the initial design phases.