Design and Optimization of Power Delivery and Distribution Systems Using Evolutionary Computation Techniques

Nowadays computing platforms consist of a very large number of components that require to be supplied with diferent voltage levels and power requirements. Even a very small platform, like a handheld computer, may contain more than twenty diferent loads and voltage regulators. The power delivery designers of these systems are required to provide, in a very short time, the right power architecture that optimizes the performance, meets electrical specifications plus cost and size targets. The appropriate selection of the architecture and converters directly defines the performance of a given solution. Therefore, the designer needs to be able to evaluate a significant number of options in order to know with good certainty whether the selected solutions meet the size, energy eficiency and cost targets. The design dificulties of selecting the right solution arise due to the wide range of power conversion products provided by diferent manufacturers. These products range from discrete components (to build converters) to complete power conversion modules that employ diferent manufacturing technologies. Consequently, in most cases it is not possible to analyze all the alternatives (combinations of power architectures and converters) that can be built. The designer has to select a limited number of converters in order to simplify the analysis. In this thesis, in order to overcome the mentioned dificulties, a new design methodology for power supply systems is proposed. This methodology integrates evolutionary computation techniques in order to make possible analyzing a large number of possibilities. This exhaustive analysis helps the designer to quickly define a set of feasible solutions and select the best trade-off in performance according to each application. The proposed approach consists of two key steps, one for the automatic generation of architectures and other for the optimized selection of components. In this thesis are detailed the implementation of these two steps. The usefulness of the methodology is corroborated by contrasting the results using real problems and experiments designed to test the limits of the algorithms.

Flying vs. climbing: Factors controlling arboreal seed removal in oak-beech forests

Nuts are heavy and nutritious seeds that need animals to be successfully dispersed. Most studies address nut removal by a single animal species once seeds fall onto the ground. However, nuts are also accessible before the seed drop and usually to a wide guild of seed foragers. This study examines the factorscontrollingarborealseedremoval in oak–beechforests within the whole guild of nut foragers. We found that seed-dispersing rodents (Apodemus sylvaticus) were the main acorn removers in the oaks (up to 3.75 m height), with a rapid seed encounter and a high removal rate. However, rodents did not climb the beech trees, probably due to their smoother bark in comparison to oak bark and/or the lower nutritional value of beechnuts with regard to acorns. Jays (Garrulus glandarius) were more abundant in oak stands (both dense and scattered) and clearly preferred acorns to beechnuts whereas nuthatches (Sitta europaea) were more abundant in beech stands and preferred beechnuts to acorns. Non-storing birds such as great tits (Parus major) also removed acorns and beechnuts, especially in the stands where oaks are dominant. Jays and rodents preferred sound seeds over insect-infested seeds but such a preference was not found for nuthatches. This study highlights that pure beech stands showed a reduced guild of arboreal nut foragers in comparison to oak stands. This different guild could probably affect the spatial patterns of seed dispersal, with a proportionally higher number of long dispersal events for acorns (mostly jay-dispersed) than for beechnuts (mostly nuthatch-dispersed). Long-distance dispersal of beechnuts (by jays) is determined by the presence of other preferred species (oaks) and their frequency of non-mast years. Seed location in different habitats strongly determines the contribution of different arboreal removers (including climbing rodents) and their removal speed, leading to a differential seed fate that will eventually affect tree regeneration. As nuthatches are sedentary birds, it is important to maintain old and dead trees where they can breed (crevices), forage (arthropods) and store seeds in order to favor beechnut dispersal and gene flow. By maintaining or favoring oak trees within beech stands we will ensure a wider guild of arboreal nut dispersers.

Grammar-Guided Evolutionary Construction of Bayesian networks

This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose grammar-guided evolutionary automatic system, whose modular structure favors its application to the automatic construction of intelligent systems. EvoBANE has been applied to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.

Using the Own Flexibility of a Climbing Robot as a Double Force Sensor

Force sensors are used when interaction tasks are carried out by robots in general, and by climbing robots in particular. If the mechanics and electronics systems are contained inside the own robot, the robot becomes portable without external control. Commercial force sensors cannot be used due to limited space and weight. By selecting the links material with appropriate stiffness and placing strain gauges on the structure, the own robot flexibility can be used such as force sensor. Thus, forces applied on the robot tip can be measured without additional external devices. Only gauges and small internal electronic converters are necessary. This paper illustrates the proposed algorithm to achieve these measurements. Additionally, experimental results are presented.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

An evolutionary algorithm for the surface structure problem

Many macroscopic properties: hardness, corrosion, catalytic activity, etc. are directly related to the surface structure, that is, to the position and chemical identity of the outermost atoms of the material. Current experimental techniques for its determination produce a “signature” from which the structure must be inferred by solving an inverse problem: a solution is proposed, its corresponding signature computed and then compared to the experiment. This is a challenging optimization problem where the search space and the number of local minima grows exponentially with the number of atoms, hence its solution cannot be achieved for arbitrarily large structures. Nowadays, it is solved by using a mixture of human knowledge and local search techniques: an expert proposes a solution that is refined using a local minimizer. If the outcome does not fit the experiment, a new solution must be proposed again. Solving a small surface can take from days to weeks of this trial and error method. Here we describe our ongoing work in its solution. We use an hybrid algorithm that mixes evolutionary techniques with trusted region methods and reuses knowledge gained during the execution to avoid repeated search of structures. Its parallelization produces good results even when not requiring the gathering of the full population, hence it can be used in loosely coupled environments such as grids. With this algorithm, the solution of test cases that previously took weeks of expert time can be automatically solved in a day or two of uniprocessor time.

Networks of Polarized Evolutionary Processors as Problem Solvers.

In this paper, we propose a solution to an NP-complete problem, namely the "3-colorability problem", based on a network of polarized processors. Our solution is uniform and time efficient.

Simulating accepting networks of evolutionary processors with filtered connections by accepting evolutionary P systems

In this work, we propose a variant of P system based on the rewriting of string-objects by means of evolutionary rules. The membrane structure of such a P system seems to be a very natural tool for simulating the filters in accepting networks of evolutionary processors with filtered connections. We discuss an informal construction supporting this simulation. A detailed proof is to be considered in an extended version of this work.

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.

Hardware-Based Particle Filter with Evolutionary Resampling Stage

Autonomous systems require, in most of the cases, reasoning and decision-making capabilities. Moreover, the decision process has to occur in real time. Real-time computing means that every situation or event has to have an answer before a temporal deadline. In complex applications, these deadlines are usually in the order of milliseconds or even microseconds if the application is very demanding. In order to comply with these timing requirements, computing tasks have to be performed as fast as possible. The problem arises when computations are no longer simple, but very time-consuming operations. A good example can be found in autonomous navigation systems with visual-tracking submodules where Kalman filtering is the most extended solution. However, in recent years, some interesting new approaches have been developed. Particle filtering, given its more general problem-solving features, has reached an important position in the field. The aim of this thesis is to design, implement and validate a hardware platform that constitutes itself an embedded intelligent system. The proposed system would combine particle filtering and evolutionary computation algorithms to generate intelligent behavior. Traditional approaches to particle filtering or evolutionary computation have been developed in software platforms, including parallel capabilities to some extent. In this work, an additional goal is fully exploiting hardware implementation advantages. By using the computational resources available in a FPGA device, better performance results in terms of computation time are expected. These hardware resources will be in charge of extensive repetitive computations. With this hardware-based implementation, real-time features are also expected.

Force sensor of a climbing robot derived from its own flexible structure

One of the most important design constraints of a climbing robot is its own weight. When links or legs are used as a locomotion system they tend to be composed of special lightweight materials, or four-bars-linkage mechanisms are designed to reduce the weight with small rigidity looses. In these cases, flexibility appears and undesirable effects, such as dynamics vibrations, must be avoided at least when the robot moves at low speeds. The knowledge of the real tip position requires the computation of its compliance or stiffness matrix and the external forces applied to the structure. Gravitational forces can be estimated, but external tip forces need to be measured. This paper proposes a strain gauge system which achieves the following tasks: (i) measurement of the external tip forces, and (ii) estimation of the real tip position (including flexibility effects). The main advantages of the proposed system are: (a) the use of external force sensors is avoided, and (b) a substantial reduction of the robot weight is achieved in comparison with other external force measurement systems. The proposed method is applied to a real symmetric climbing robot and experimental results are presented.

Dynamic control of a reconfigurable stair-climbing mobility system

Electric-powered wheelchairs improve the mobility of people with physical disabilities, but the problem to deal with certain architectural barriers has not been resolved satisfactorily. In order to solve this problem, a stair-climbing mobility system (SCMS) was developed. This paper presents a practical dynamic control system that allows the SCMS to exhibit a successful climbing process when faced with typical architectural barriers such as curbs, ramps, or staircases. The implemented control system depicts high simplicity, computational efficiency, and the possibility of an easy implementation in a microprocessor-/microcontroller-based system. Finally, experiments are included to support theoretical results.