253 research outputs found
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Generalized Lorenz-Mie theory : application to scattering and resonances of photonic complexes
Les structures photoniques complexes permettent de façonner la propagation lumineuse Ă lâĂ©chelle de la longueur dâonde au moyen de processus de diffusion et dâinterfĂ©rence. Cette fonctionnalitĂ© Ă lâĂ©chelle nanoscopique ouvre la voie Ă de multiples applications, allant des communications optiques aux biosenseurs. Cette thĂšse porte principalement sur la modĂ©lisation numĂ©rique de structures photoniques complexes constituĂ©es dâarrangements bidimensionnels de cylindres diĂ©lectriques. Deux applications sont privilĂ©giĂ©es, soit la conception de dispositifs basĂ©s sur des cristaux photoniques pour la manipulation de faisceaux, de mĂȘme que la rĂ©alisation de sources lasers compactes basĂ©es sur des molĂ©cules photoniques. Ces structures optiques peuvent ĂȘtre analysĂ©es au moyen de la thĂ©orie de Lorenz-Mie gĂ©nĂ©ralisĂ©e, une mĂ©thode numĂ©rique permettant dâexploiter la symĂ©trie cylindrique des diffuseurs sous-jacents. Cette dissertation dĂ©bute par une description de la thĂ©orie de Lorenz-Mie gĂ©nĂ©ralisĂ©e, obtenue des Ă©quations de Maxwell de lâĂ©lectromagnĂ©tisme. Dâautres outils thĂ©oriques utiles sont Ă©galement prĂ©sentĂ©s, soit une nouvelle formulation des Ă©quations de Maxwell-Bloch pour la modĂ©lisation de milieux actifs appelĂ©e SALT (steady state ab initio laser theory). Une description sommaire des algorithmes dâoptimisation dits mĂ©taheuristiques conclut le matĂ©riel introductif de la thĂšse. Nous prĂ©sentons ensuite la conception et lâoptimisation de dispositifs intĂ©grĂ©s permettant la gĂ©nĂ©ration de faisceaux dâamplitude, de phase et de degrĂ© de polarisation contrĂŽlĂ©s. Le problĂšme dâoptimisation combinatoire associĂ© est solutionnĂ© numĂ©riquement au moyen de deux mĂ©taheuristiques, lâalgorithme gĂ©nĂ©tique et la recherche tabou. Une Ă©tude thĂ©orique des propriĂ©tĂ©s de micro-lasers basĂ©s sur des molĂ©cules photoniques â constituĂ©es dâun arrangement simple de cylindres actifs â est finalement prĂ©sentĂ©e. En combinant la thĂ©orie de Lorenz-Mie et SALT, nous dĂ©montrons que les propriĂ©tĂ©s physiques de ces lasers, plus spĂ©cifiquement leur seuil, leur spectre et leur profil dâĂ©mission, peuvent ĂȘtre affectĂ©s de façon nontriviale par les paramĂštres du milieu actif sous-jacent. Cette conclusion est hors dâatteinte de lâapproche Ă©tablie qui consiste Ă calculer les Ă©tatsmĂ©ta-stables de lâĂ©quation de Helmholtz et leur facteur de qualitĂ©. Une perspective sur la modĂ©lisation de milieux photoniques dĂ©sordonnĂ©s conclut cette dissertation.Complex photonic media mold the flow of light at the wavelength scale using multiple scattering and interference effects. This functionality at the nano-scale level paves the way for various applications, ranging from optical communications to biosensing. This thesis is mainly concerned with the numerical modeling of photonic complexes based on twodimensional arrays of cylindrical scatterers. Two applications are considered, namely the use of photonic-crystal-like devices for the design of integrated beam shaping elements, as well as active photonic molecules for the realization of compact laser sources. These photonic structures can be readily analyzed using the 2D Generalized Lorenz-Mie theory (2D-GLMT), a numerical scheme which exploits the symmetry of the underlying cylindrical structures. We begin this thesis by presenting the electromagnetic theory behind 2D-GLMT.Other useful frameworks are also presented, including a recently formulated stationary version of theMaxwell-Bloch equations called steady-state ab initio laser theory (SALT).Metaheuristics, optimization algorithms based on empirical rules for exploring large solution spaces, are also discussed. After laying down the theoretical content, we proceed to the design and optimization of beam shaping devices based on engineered photonic-crystal-like structures. The combinatorial optimization problem associated to beam shaping is tackled using the genetic algorithm (GA) as well as tabu search (TS). Our results show the possibility to design integrated beam shapers tailored for the control of the amplitude, phase and polarization profile of the output beam. A theoretical and numerical study of the lasing characteristics of photonic molecules â composed of a few coupled optically active cylinders â is also presented. Using a combination of 2D-GLMT and SALT, it is shown that the physical properties of photonic molecule lasers, specifically their threshold, spectrum and emission profile, can be significantly affected by the underlying gain medium parameters. These findings are out of reach of the established approach of computing the meta-stable states of the Helmholtz equation and their quality factor. This dissertation is concluded with a research outlook concerning themodeling of disordered photonicmedia
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Recommended from our members
Surrogate Model Optimisation for PWR Fuel Management
Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the âcurse of dimensionalityâ, which makes these complex problems difficult to investigate.
In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a âstandardâ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware.
The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering
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
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
Particle Swarm Optimization
Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Advances in Optimization and Nonlinear Analysis
The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conwayâs life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MRâs applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithmsâ performance on Amazonâs Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
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