92 research outputs found

    A Multiobjective Approach Applied to the Protein Structure Prediction Problem

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    Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations

    Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

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    This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

    Evolutionary Computation

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    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

    A new ant colony optimization model for complex graph-based problems

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    Tesis doctoral inĂ©dita leĂ­da en la Universidad AutĂłnoma de Madrid. Escuela PolitĂ©cnica Superior, Departamento de IngenierĂ­a InformĂĄtica. Fecha de lectura: julio de 2014Nowadays, there is a huge number of problems that due to their complexity have employed heuristic-based algorithms to search for near-to-optimal (or even optimal) solutions. These problems are usually NP-complete, so classical algorithms are not the best candidates to address these problems because they need a large amount of computational resources, or they simply cannot find any solution when the problem grows. Some classical examples of these kind of problems are the Travelling Salesman Problem (TSP) or the N-Queens problem. It is also possible to find examples in real and industrial domains related to the optimization of complex problems, like planning, scheduling, Vehicle Routing Problems (VRP), WiFi network Design Problem (WiFiDP) or behavioural pattern identification, among others. Regarding to heuristic-based algorithms, two well-known paradigms are Swarm Intelligence and Evolutionary Computation. Both paradigms belongs to a subfield from Artificial Intelligence, named Computational Intelligence that also contains Fuzzy Systems, Artificial Neural Networks and Artificial Immune Systems areas. Swarm Intelligence (SI) algorithms are focused on the collective behaviour of selforganizing systems. These algorithms are characterized by the generation of collective intelligence from non-complex individual behaviour and the communication schemes amongst them. Some examples of SI algorithms are particle swarm optimization, ant colony optimization (ACO), bee colony optimization o bird flocking. Ant Colony Optimization (ACO) are based on the foraging behaviour of these insects. In these kind of algorithms, the ants take different decisions during their execution that allows them to build their own solution to the problem. Once any ant has finished its execution, the ant goes back through the followed path and it deposits, in the environment, pheromones that contains information about the built solution. These pheromones will influence the decision of future ants, so there is an indirect communication through the environment called stigmergy. When an ACO algorithm is applied to any of the optimization problems just described, the problem is usually modelled into a graph. Nevertheless, the classical graph-based representation is not the best one for the execution of ACO algorithms because it presents some important pitfalls. The first one is related to the polynomial, or even exponential, growth of the resulting graph. The second pitfall is related to those problems that needs from real variables because these problems cannot be modelled using the classical graph-based representation. On the other hand, Evolutionary Computation (EC) are a set of population-based algorithms based in the Darwinian evolutionary process. In this kind of algorithms there is one (or more) population composed by different individuals that represent a possible solution to the problem. For each iteration, the population evolves by the use of evolutionary procedures which means that better individuals (i.e. better solutions) are generated along the execution of the algorithm. Both kind of algorithms, EC and SI, have been traditionally applied in previous NP-hard problems. Different population-based strategies have been developed, compared and even combined to design hybrid algorithms. This thesis has been focused on the analysis of classical graph-based representations and its application in ACO algorithms into complex problems, and the development of a new ACO model that tries to take a step forward in this kind of algorithms. In this new model, the problem is represented using a reduced graph that affects to the ants behaviour, which becomes more complex. Also, this size reduction generates a fast growth in the number of pheromones created. For this reason, a new metaheuristic (called Oblivion Rate) has been designed to control the number of pheromones stored in the graph. In this thesis different metaheuristics have been designed for the proposed system and their performance have been compared. One of these metaheuristics is the Oblivion Rate, based on an exponential function that takes into account the number of pheromones created in the system. Other Oblivion Rate function is based on a bioinspired swarm algorithm that uses some concepts extracted from the evolutionary algorithms. This bio-inspired swarm algorithm is called Coral Reef Opmization (CRO) algorithm and it is based on the behaviour of the corals in a reef. Finally, to test and validate the proposed model, different domains have been used such as the N-Queens Problem, the Resource-Constraint Project Scheduling Problem, the Path Finding problem in Video Games, or the Behavioural Pattern Identification in users. In some of these domains, the performance of the proposed model has been compared against a classical Genetic Algorithm to provide a comparative study and perform an analytical comparison between both approaches.En la actualidad, existen un gran nĂșmero de problemas que debido a su complejidad necesitan algoritmos basados en heurĂ­sticas para la bĂșsqueda de solucionas subĂłptimas (o incluso Ăłptimas). Normalmente, estos problemas presentan una complejidad NP-completa, por lo que los algoritmos clĂĄsicos de bĂșsqueda de soluciones no son apropiados ya que necesitan una gran cantidad de recursos computacionales, o simplemente, no son capaces de encontrar alguna soluciĂłn cuando el problema crece. Ejemplos clĂĄsicos de este tipo de problemas son el problema del vendedor viajero (o TSP del inglĂ©s Travelling Salesman Problem) o el problema de las N-reinas. TambiĂ©n se pueden encontrar ejemplos en dominios reales o industriales que generalmente estĂĄn ligados a temas de optimizaciĂłn de sistemas complejos, como pueden ser problemas de planificaciĂłn, scheduling, problemas de enrutamiento de vehĂ­culos (o VRP del inglĂ©s Vehicle Routing Problem), el diseño de redes Wifi abiertas (o WiFiDP del inglĂ©s WiFi network Design Problem), o la identificaciĂłn de patrones de comportamiento, entre otros. En lo referente a los algoritmos basados en heuristicas, dos paradigmas muy conocidos son los algoritmos de enjambre (Swarm Intelligence) y la computaciĂłn evolutiva (Evolutionary Computation). Ambos paradigmas pertencen al subĂĄrea de la Inteligencia Artificial denominada Inteligencia Computacional, que ademĂĄs contiene los sistemas difusos, redes neuronales y sistemas inmunolĂłgicos artificiales. Los algoritmos de inteligencia de enjambre, o Swarm Intelligence, se centran en el comportamiento colectivo de sistemas auto-organizativos. Estos algoritmos se caracterizan por la generaciĂłn de inteligencia colectiva a partir del comportamiento, no muy complejo, de los individuos y los esquemas de comunicaciĂłn entre ellos. Algunos ejemplos son particle swarm optimization, ant colony optimization (ACO), bee colony optimization o bird flocking. Los algoritmos de colonias de hormigas (o ACO del inglĂ©s Ant Colony Optimization) se basan en el comportamiento de estos insectos en el proceso de recolecciĂłn de comida. En este tipo de algoritmos, las hormigas van tomando decisiones a lo largo de la simulaciĂłn que les permiten construir su propia soluciĂłn al problema. Una vez que una hormiga termina su ejecuciĂłn, deshace el camino andado depositando en el entorno feronomas que contienen informaciĂłn sobre la soluciĂłn construida. Estas feromonas influirĂĄn en las decisiones de futuras hormigas, por lo que produce una comunicaciĂłn indirecta utilizando el entorno. A este proceso se le llama estigmergia. Cuando un algoritmo de hormigas se aplica a alguno de los problemas de optimizaciĂłn descritos anteriormente, se suele modelar el problema como un grafo sobre el cual se ejecutarĂĄn las hormigas. Sin embargo, la representaciĂłn basada en grafos clĂĄsica no parece ser la mejor para la ejecuciĂłn de algoritmos de hormigas porque presenta algunos problemas importantes. El primer problema estĂĄ relacionado con el crecimiento polinĂłmico, o incluso expnomencial, del grafo resultante. El segundo problema tiene que ver con los problemas que necesitan de variables reales, o de coma flotante, porque estos problemas, con la representaciĂłn tradicional basada en grafos, no pueden ser modelados. Por otro lado, los algoritmos evolutivos (o EC del inglĂ©s Evolutionary Computation) son un tipo de algoritmos basados en poblaciĂłn que estĂĄn inspirados en el proceso evolutivo propuesto por Darwin. En este tipo de algoritmos, hay una, o varias, poblaciones compuestas por individuos diferentes que representan problems solutiones al problema modelado. Por cada iteraciĂłn, la poblaciĂłn evoluciona mediante el uso de procedimientos evolutivos, lo que significa que mejores individuos (mejores soluciones) son creados a lo largo de la ejecuciĂłn del algoritmo. Ambos tipos de algorithmos, EC y SI, han sido tradicionalmente aplicados a los problemas NPcompletos descritos anteriormente. Diferentes estrategias basadas en poblaciĂłn han sido desarrolladas, comparadas e incluso combinadas para el diseño de algoritmos hĂ­bridos. Esta tesis se ha centrado en el anĂĄlisis de los modelos clĂĄsicos de representaciĂłn basada en grafos de problemas complejos para la posterior ejecuciĂłn de algoritmos de colonias de hormigas y el desarrollo de un nuevo modelo de hormigas que pretende suponer un avance en este tipo de algoritmos. En este nuevo modelo, los problemas son representados en un grafo mĂĄs compacto que afecta al comportamiento de las hormigas, el cual se vuelve mĂĄs complejo. AdemĂĄs, esta reducciĂłn en el tamaño del grafo genera un rĂĄpido crecimiento en el nĂșmero de feronomas creadas. Por esta razĂłn, una nueva metaheurĂ­stica (llamada Oblivion Rate) ha sido diseñada para controlar el nĂșmero de feromonas almacenadas en el grafo. En esta tesis, varias metaheuristicas han sido diseñadas para el sistema propuesto y sus rendimientos han sido comparados. Una de estas metaheurĂ­sticas es la Oblivion Rate basada en una funciĂłn exponencial que tiene en cuenta el nĂșmero de feromonas creadas en el sistema. Otra Oblivion Rate estĂĄ basada en un algoritmo de enjambre bio-inspirado que usa algunos conceptos extraĂ­dos de la computaciĂłn evolutiva. Este algoritmo de enjambre bio-inspirado se llama OptimizaciĂłn de arrecifes de corales (o CRO del inglĂ©s Coral Reef Optimization) y estĂĄ basado en el comportamiento de los corales en el arrecife. Finalmente, para validar y testear el modelo propuesto, se han utilizado diversos dominios de aplicaciĂłn como son el problema de las N-reinas, problemas de planificaciĂłn de proyectos con restricciones de recursos, problemas de bĂșsqueda de caminos en entornos de videojuegos y la identificaciĂłn de patrones de comportamiento de usuarios. En algunos de estos dominios, el rendimiento del modelo propuesto ha sido comparado contra un algoritmo genĂ©tico clĂĄsico para realizar un estudio comparativo, y analĂ­tico, entre ambos enfoques

    Project schedule optimisation utilising genetic algorithms

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    This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    A Multi-objective Bi-level Optimisation model for Agricultural Policy in Scotland

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    Agricultural policy analysis can be visualised as a multiple objective hierarchical optimisation problem whereby sequential non-cooperative interactions between the policy makers and the farmers take place. The objectives and choices of policy makers will almost always diverge from the objectives and choices of farmers. Policy makers exercise authority over some, but not all, of the variables in the total system whereas other variables affecting their multiple goals are under the direct control of myriad farmers who operate according to their own utility maximising motives. In order to advance their own objectives, the policy makers unilaterally and pre-emptively set the policy measures to influence the farmers. The farmers execute their decisions after, and in view of, the policies and make their production decisions that observe their goals best. Ultimately, the payoffs to both the policy makers and the farmers depend not only on the actions of the former, but also on the reactions of the latter. Such problems are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This thesis shows how multi-objective genetic algorithms (MOGA) in conjunction with mathematical programming (MP) can be used for solving this type of problems. A MP model is developed to capture the production choices of farmers. The model is based on positive mathematical programming and its objective function parameters are estimated using the method of generalised maximum entropy. The model is nested in and controlled by a MOGA which captures the process of multi-objective optimisation of policy decisions. The approach is illustrated using a case study taken from the Scottish agricultural systems, where several socio-economic and environmental objectives for policy making are considered. Four types of policy instruments are examined: the current single payment scheme, a multi-payment scheme based on land use, an input taxation and a regulatory scheme. For a selection of scenarios alternative Pareto-optimal solutions are discovered and tradeoffs between the policy objectives are presented along with their associated production patterns. The performance of the modelling tool developed suggests that it is well suited to dealing with real-world policy issues. It offers considerable possibilities for exploring tradeoffs between non-commensurable and conflicting objectives relevant to sustainable development of Scottish agriculture

    Effective integrations of constraint programming, integer programming and local search for two combinatorial optimisation problems

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    This thesis focuses on the construction of effective and efficient hybrid methods based on the integrations of Constraint Programming (CP), Integer Programming (IP) and local search (LS) to tackle two combinatorial optimisation problems from different application areas: the nurse rostering problems and the portfolio selection problems. The principle of designing hybrid methods in this thesis can be described as: for the combinatorial problems to be solved, the properties of the problems are investigated firstly and the problems are decomposed accordingly in certain ways; then the suitable solution techniques are integrated to solve the problem based on the properties of substructures/subproblems by taking the advantage of each technique. For the over-constrained nurse rostering problems with a large set of complex constraints, the problems are first decomposed by constraint. That is, only certain selected set of constraints is considered to generate feasible solutions at the first stage. Then the rest of constraints are tackled by a second stage local search method. Therefore, the hybrid methods based on this constraint decomposition can be represented by a two-stage framework “feasible solution + improvement”. Two integration methods are proposed and investigated under this framework. In the first integration method, namely a hybrid CP with Variable Neighourhood Search (VNS) approach, the generation of feasible initial solutions relies on the CP while the improvement of initial solutions is gained by a simple VNS in the second stage. In the second integration method, namely a constraint-directed local search, the local search is enhanced by using the information of constraints. The experimental results demonstrate the effectiveness of these hybrid approaches. Based on another decomposition method, Dantzig-Wolfe decomposition, in the third integration method, a CP based column generation, integrates the feasibility reasoning of CP with the relaxation and optimality reasoning of Linear Programming. The experimental results demonstrate the effectiveness of the methods as well as the knowledge of the quality of the solution. For the portfolio selection problems, two integration methods, which integrate Branch-and-Bound algorithm with heuristic search, are proposed and investigated. In layered Branch-and-Bound algorithm, the problem is decomposed into the subsets of variables which are considered at certain layers in the search tree according to their different features. Node selection heuristics, and branching rules, etc. are tailored to the individual layers, which speed up the search to the optimal solution in a given time limit. In local search branching Branch-and-Bound algorithm, the idea of local search is applied as the branching rule of Branch-and-Bound. The local search branching is applied to generate a sequence of subproblems. The procedure for solving these subproblems is accelerated by means of the solution information reusing. This close integration between local search and Branch-and-Bound improves the efficiency of the Branch-and-Bound algorithm according to the experimental results. The hybrid approaches benefit from each component which is selected according to the properties of the decomposed problems. The effectiveness and efficiency of all the hybrid approaches to the two application problems developed in this thesis are demonstrated. The idea of designing appropriate components in hybrid approach concerning properties of subproblems is a promising methodology with extensive potential applications in other real-world combinatorial optimisation problems

    Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis

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    Circuit Optimisation using Device Layout Motifs

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    Circuit designers face great challenges as CMOS devices continue to scale to nano dimensions, in particular, stochastic variability caused by the physical properties of transistors. Stochastic variability is an undesired and uncertain component caused by fundamental phenomena associated with device structure evolution, which cannot be avoided during the manufacturing process. In order to examine the problem of variability at atomic levels, the 'Motif' concept, defined as a set of repeating patterns of fundamental geometrical forms used as design units, is proposed to capture the presence of statistical variability and improve the device/circuit layout regularity. A set of 3D motifs with stochastic variability are investigated and performed by technology computer aided design simulations. The statistical motifs compact model is used to bridge between device technology and circuit design. The statistical variability information is transferred into motifs' compact model in order to facilitate variation-aware circuit designs. The uniform motif compact model extraction is performed by a novel two-step evolutionary algorithm. The proposed extraction method overcomes the drawbacks of conventional extraction methods of poor convergence without good initial conditions and the difficulty of simulating multi-objective optimisations. After uniform motif compact models are obtained, the statistical variability information is injected into these compact models to generate the final motif statistical variability model. The thesis also considers the influence of different choices of motif for each device on circuit performance and its statistical variability characteristics. A set of basic logic gates is constructed using different motif choices. Results show that circuit performance and variability mitigation can benefit from specific motif permutations. A multi-stage optimisation methodology is introduced, in which the processes of optimisation are divided into several stages. Benchmark circuits show the efficacy of the proposed methods. The results presented in this thesis indicate that the proposed methods are able to provide circuit performance improvements and are able to create circuits that are more robust against variability
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