Robust Mission Design Through Evidence Theory and Multi-Agent Collaborative Search

In this paper, the preliminary design of a space mission is approached introducing uncertainties on the design parameters and formulating the resulting reliable design problem as a multiobjective optimization problem. Uncertainties are modelled through evidence theory and the belief, or credibility, in the successful achievement of mission goals is maximised along with the reliability of constraint satisfaction. The multiobjective optimisation problem is solved through a novel algorithm based on the collaboration of a population of agents in search for the set of highly reliable solutions. Two typical problems in mission analysis are used to illustrate the proposed methodology

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance

Optimizing propeller performance: a comprehensive constrained multi-objective design approach using blade element theory and evolutionary algorithms

Currently, concerns about energy efficiency, sustainability, and the environment are growing, as proposed by the UN through The Global Goals (Goal 9 - Industry, Innovation and Infrastructure), and the search for new, more efficient, and cleaner solutions is notorious in all engineering fields. With the advancement of new manufacturing techniques, discovery and development of new materials, and expansion of computational capabilities, new opportunities for study in the field of aeronautical engineering arise. The present work proposes the elaboration of a new methodology to be used in the design and optimization of propellers, as well as the algorithms and couplings necessary for its accomplishment. During the work, a search was made for aerodynamic profiles, and a database was created with about 1500 of these. Such profiles had their coordinates standardized and refined. All airfoils were analyzed using panel methods through the XFOIL code. Using the evolutionary algorithms AGEMOEA, ARMOEA, MSOPSII, NSGAII, and NSGAIIARSBX present in the PlatEMO optimization platform coupled to the propeller analysis program JAVAPROP, in addition to the coupling of a structural analysis code, optimizations were performed for different objectives. This set of elements was added to PlatEMO as a problem and named OptProp. Initially, analyzes were carried out taking into account only the dimensionless parameters of the propellers, through seven different multi-objective optimization problems for two different powertrain groups. Then, an optimization is performed to minimize the power required for two different flight points and a propeller is selected from the Pareto front obtained. With such a propeller established, operational optimization is sought for a given mission by varying the rotational speed of the system. In all optimizations, geometric constraints are considered, and, in operational optimization, structural constraints through natural frequencies and the Campbell diagram are also used. Energy savings close to 1.4% were found after operational optimization.Atualmente são crescentes as preocupações com eficiência energética, sustentabilidade e com o meio ambiente, como proposto pela ONU através do The Global Goals (Goal 9 - Industry, Innovation and Infrastructure), e a busca por novas soluções mais eficientes e limpas é notória em todos os campos da engenharia. Com o avanço de novas técnicas de manufatura, descoberta e desenvolvimento de novos materiais e ampliação das capacidades computacionais surgem novas oportunidades de estudo no campo da engenharia aeronáutica. O presente trabalho propõe a elaboração de uma nova metodologia a ser utilizada no projeto e otimização de hélices, bem como os algoritmos e acoplamentos necessários. Foi realizada uma busca por perfis aerodinâmicos e composto um banco de dados com cerca de 1500 desses. Tais perfis tiveram suas coordenadas padronizadas e refinadas. Todos os perfis aerodinâmicos foram analisados através do método dos painéis utilizando o código XFOIL. Utilizando os algoritmos evolucionários AGEMOEA, ARMOEA, MSOPSII, NSGAII e NSGAIIARSBX presentes na plataforma de otimização PlatEMO acoplada ao programa de análise de hélices JAVAPROP, além do acoplamento de um código de análise estrutural, foram realizadas otimizações para diferentes objetivos. Esse conjunto de elementos foi adicionado ao PlatEMO como um problema e batizado de OptProp. Inicialmente, foram realizadas análises levando em conta apenas os parâmetros adimensionais das hélices, através de sete diferentes problemas de otimização multiobjetivo para dois diferentes grupos motopropulsores. Em seguida, é realizada uma otimização que busca a minimização da potência requerida para dois diferentes pontos de voo e uma hélice é selecionada da frente de Pareto obtida. Com tal hélice selecionada, busca-se uma otimização operacional para uma determinada missão através da variação da velocidade rotacional do conjunto motopropulsor. Em todas as otimizações são consideradas restrições geométricas e, na otimização operacional, é utilizado também restrições estruturais através de frequências naturais e diagrama de Campbell. Foram encontrados economias de energia próximas de 1, 4% após a otimização operacional.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Stochastic search methodologies for multi-objective simulation optimization

Ph.DDOCTOR OF PHILOSOPH

Multi-Objective Optimization Methods Based on Artificial Neural Networks

Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer. This book demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. Although it is targeted to a wide group of readers: researchers, graduate students, and practitioners, it does not offer an exhaustive coverage of search algorithms and applications. The chapters are organized into three parts: Population-based and quantum search algorithms, Search algorithms for image and video processing, and Search algorithms for engineering applications

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS

El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field