Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.

Assessing the accessibility impact of transport policy by a land-use and transport interaction model - The case of Madrid

Accessibility is an essential concept widely used to evaluate the impact of transport and land-use strategies in urban planning and policy making. Accessibility is typically evaluated by using separately a transport model or a land-use model. This paper embeds two accessibility indicators (i.e., potential and adaptive accessibility) in a land use and transport interaction (LUTI) model in order to assess transport policies implementation. The first aim is to define the adaptive accessibility, considering the competition factor at territorial level (e.g. workplaces and workers). The second aim is to identify the optimal implementation scenario of policy measures using potential and adaptive accessibility indicators. The analysis of the results in terms of social welfare and accessibility changes closes the paper. Two transport policy measures are applied in Madrid region: a cordon toll and increase bus frequency. They have been simulated through the MARS model (Metropolitan Activity Relocation Simulator, i.e. LUTI model). An optimisation procedure is performed by MARS for maximizing the value of the objective function in order to find the optimal policy implementation (first best). Both policy measures are evaluated in terms of accessibility. Results show that the introduction of the accessibility indicators (potential and adaptive) influence the optimal value of the toll price and bus frequency level, generating different results in terms of social welfare. Mapping the difference between potential and adaptive accessibility indicator shows that the main changes occur in areas where there is a strong competition among different land-use opportunities.

Two-dimensional isostatic meshes in the finite element method

In a Finite Element (FE) analysis of elastic solids several items are usually considered, namely, type and shape of the elements, number of nodes per element, node positions, FE mesh, total number of degrees of freedom (dot) among others. In this paper a method to improve a given FE mesh used for a particular analysis is described. For the improvement criterion different objective functions have been chosen (Total potential energy and Average quadratic error) and the number of nodes and dof's of the new mesh remain constant and equal to the initial FE mesh. In order to find the mesh producing the minimum of the selected objective function the steepest descent gradient technique has been applied as optimization algorithm. However this efficient technique has the drawback that demands a large computation power. Extensive application of this methodology to different 2-D elasticity problems leads to the conclusion that isometric isostatic meshes (ii-meshes) produce better results than the standard reasonably initial regular meshes used in practice. This conclusion seems to be independent on the objective function used for comparison. These ii-meshes are obtained by placing FE nodes along the isostatic lines, i.e. curves tangent at each point to the principal direction lines of the elastic problem to be solved and they should be regularly spaced in order to build regular elements. That means ii-meshes are usually obtained by iteration, i.e. with the initial FE mesh the elastic analysis is carried out. By using the obtained results of this analysis the net of isostatic lines can be drawn and in a first trial an ii-mesh can be built. This first ii-mesh can be improved, if it necessary, by analyzing again the problem and generate after the FE analysis the new and improved ii-mesh. Typically, after two first tentative ii-meshes it is sufficient to produce good FE results from the elastic analysis. Several example of this procedure are presented.

Train scheduling and rolling stock assignment in high speed trains

The Train Timetabling Problem (TTP) has been widely studied for freight and passenger rail systems. A lesser effort has been devoted to the study of high-speed rail systems. A modeling issue that has to be addressed is to model departure time choice of passengers on railway services. Passengers who use these systems attempt to travel at predetermined hours due to their daily life necessities (e.g., commuter trips). We incorporate all these features into TTP focusing on high-speed railway systems. We propose a Rail Scheduling and Rolling Stock (RSch-RS) model for timetable planning of high-speed railway systems. This model is composed of two essential elements: i) an infrastructure model for representing the railway network: it includes capacity constraints of the rail network and the Rolling-Stock constraints; and ii) a demand model that defines how the passengers choose the departure time. The resulting model is a mixed-integer programming model which objective function attempts to maximize the profit for the rail operator

Two near-optimal layouts for trusslike bridge structures bearing uniform weight between supports

Although the primary objective on designing a structure is to support the external loads, the achievement of an optimal layout that reduces all costs associated with the structure is an aspect of increasing interest. The problem of finding the optimal layout for bridgelike structures subjected to a uniform load is considered. The problem is formulated following a theory on economy of frame structures, using the stress volume as the objective function and including the selection of appropriate values for statically indeterminate reactions. It is solved in a function space of finite dimension instead of using a general variational approach, obtaining near-optimal solutions. The results obtained with this profitable strategy are very close to the best layouts known to date, with differences of less than 2% for the stress volume, but with a simpler layout that can be recognized in some real bridges. This strategy could be a guide to preliminary design of bridges subject to a wide class of costs.

Design and Multi-Objective Optimization of EMI Input Filters

An EMI filter design procedure for power converters is proposed. Based on a given noise spectrum, information about the converter noise source impedance and design constraints, the design space of the input filter is defined. The design is based on component databases and detailed models of the filter components, including high frequency parasitics, losses, weight, volume, etc.. The design space is mapped onto a performance space in which different filter implementations are evaluated and compared. A multi-objective optimization approach is used to obtain optimal designs w.r.t. a given performance function.

A 3D multi-objective optimization planning algorithm for wireless sensor networks

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

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.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

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.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Objective Assessment of Olfactory Function Using Functional Magnetic Resonance Imaging=Estudio objetivo del olfato mediante resonancia magnética funcional

Objective: To show the results of a device that generates automated olfactory stimuli suitable for functional magnetic resonance imaging (fMRI) experiments. Material and methods: Te n normal volunteers, 5 women and 5 men, were studied. The system allows the programming of several sequences, providing the capability to synchronise the onset of odour presentation with acquisition by a trigger signal of the MRI scanner. The olfactometer is a device that allows selection of the odour, the event paradigm, the time of stimuli and the odour concentration. The paradigm used during fMRI scanning consisted of 15-s blocks. The odorant event took 2 s with butanol, mint and coffee. Results: We observed olfactory activity in the olfactory bulb, entorhinal cortex (4%), amygdala (2.5%) and temporo-parietal cortex, especially in the areas related to emotional integration. Conclusions: The device has demonstrated its effectiveness in stimulating olfactory areas and its capacity to adapt to fMRI equipment.RESUMEN Objetivo: Mostrar los resultados del olfatómetro capaz de generar tareas olfativas en un equipo de resonancia magnética funcional (fMRI). Material y métodos: Estudiamos 10 sujetos normales: 5 varones y 5 mujeres. El olfatómetro está dise ̃ nado para que el estímulo que produce se sincronice con el equipo de fMRI mediante la se ̃ nal desencadenante que suministra el propio equipo. El olfatómetro es capaz de: selec- cionar el olor, secuenciar los distintos olores, programar la frecuencia y duración de los olores y controlar la intensidad del olor. El paradigma utilizado responde a un dise ̃ no de activación asociada a eventos, en el que la duración del bloque de activación y de reposo es de 15 s. La duración del estímulo olfativo (butanol, menta o café) es de 2 segundos, durante toda la serie que consta de 9 ciclos. Resultados: Se ha observado reactividad (contraste BOLD) en las diferentes áreas cerebrales involucradas en las tareas olfativas: bulbo olfatorio, córtex entorrinal (4%), amigdala (2,5%) y córtex temporoparietal. Las áreas relacionadas con integración de las emociones tienen una reactividad mayor. Conclusiones: El dispositivo propuesto nos permite controlar de forma automática y sincronizada los olores necesarios para estudiar la actividad de las áreas olfatorias cerebrales mediante fMRI.

Design and control of multi-finger haptic devices for dexterous manipulation

En la interacción con el entorno que nos rodea durante nuestra vida diaria (utilizar un cepillo de dientes, abrir puertas, utilizar el teléfono móvil, etc.) y en situaciones profesionales (intervenciones médicas, procesos de producción, etc.), típicamente realizamos manipulaciones avanzadas que incluyen la utilización de los dedos de ambas manos. De esta forma el desarrollo de métodos de interacción háptica multi-dedo dan lugar a interfaces hombre-máquina más naturales y realistas. No obstante, la mayoría de interfaces hápticas disponibles en el mercado están basadas en interacciones con un solo punto de contacto; esto puede ser suficiente para la exploración o palpación del entorno pero no permite la realización de tareas más avanzadas como agarres. En esta tesis, se investiga el diseño mecánico, control y aplicaciones de dispositivos hápticos modulares con capacidad de reflexión de fuerzas en los dedos índice, corazón y pulgar del usuario. El diseño mecánico de la interfaz diseñada, ha sido optimizado con funciones multi-objetivo para conseguir una baja inercia, un amplio espacio de trabajo, alta manipulabilidad y reflexión de fuerzas superiores a 3 N en el espacio de trabajo. El ancho de banda y la rigidez del dispositivo se han evaluado mediante simulación y experimentación real. Una de las áreas más importantes en el diseño de estos dispositivos es el efector final, ya que es la parte que está en contacto con el usuario. Durante este trabajo se ha diseñado un dedal de bajo peso, adaptable a diferentes usuarios que, mediante la incorporación de sensores de contacto, permite estimar fuerzas normales y tangenciales durante la interacción con entornos reales y virtuales. Para el diseño de la arquitectura de control, se estudiaron los principales requisitos para estos dispositivos. Entre estos, cabe destacar la adquisición, procesado e intercambio a través de internet de numerosas señales de control e instrumentación; la computación de equaciones matemáticas incluyendo la cinemática directa e inversa, jacobiana, algoritmos de detección de agarres, etc. Todos estos componentes deben calcularse en tiempo real garantizando una frecuencia mínima de 1 KHz. Además, se describen sistemas para manipulación de precisión virtual y remota; así como el diseño de un método denominado "desacoplo cinemático iterativo" para computar la cinemática inversa de robots y la comparación con otros métodos actuales. Para entender la importancia de la interacción multimodal, se ha llevado a cabo un estudio para comprobar qué estímulos sensoriales se correlacionan con tiempos de respuesta más rápidos y de mayor precisión. Estos experimentos se desarrollaron en colaboración con neurocientíficos del instituto Technion Israel Institute of Technology. Comparando los tiempos de respuesta en la interacción unimodal (auditiva, visual y háptica) con combinaciones bimodales y trimodales de los mismos, se demuestra que el movimiento sincronizado de los dedos para generar respuestas de agarre se basa principalmente en la percepción háptica. La ventaja en el tiempo de procesamiento de los estímulos hápticos, sugiere que los entornos virtuales que incluyen esta componente sensorial generan mejores contingencias motoras y mejoran la credibilidad de los eventos. Se concluye que, los sistemas que incluyen percepción háptica dotan a los usuarios de más tiempo en las etapas cognitivas para rellenar información de forma creativa y formar una experiencia más rica. Una aplicación interesante de los dispositivos hápticos es el diseño de nuevos simuladores que permitan entrenar habilidades manuales en el sector médico. En colaboración con fisioterapeutas de Griffith University en Australia, se desarrolló un simulador que permite realizar ejercicios de rehabilitación de la mano. Las propiedades de rigidez no lineales de la articulación metacarpofalange del dedo índice se estimaron mediante la utilización del efector final diseñado. Estos parámetros, se han implementado en un escenario que simula el comportamiento de la mano humana y que permite la interacción háptica a través de esta interfaz. Las aplicaciones potenciales de este simulador están relacionadas con entrenamiento y educación de estudiantes de fisioterapia. En esta tesis, se han desarrollado nuevos métodos que permiten el control simultáneo de robots y manos robóticas en la interacción con entornos reales. El espacio de trabajo alcanzable por el dispositivo háptico, se extiende mediante el cambio de modo de control automático entre posición y velocidad. Además, estos métodos permiten reconocer el gesto del usuario durante las primeras etapas de aproximación al objeto para su agarre. Mediante experimentos de manipulación avanzada de objetos con un manipulador y diferentes manos robóticas, se muestra que el tiempo en realizar una tarea se reduce y que el sistema permite la realización de la tarea con precisión. Este trabajo, es el resultado de una colaboración con investigadores de Harvard BioRobotics Laboratory. ABSTRACT When we interact with the environment in our daily life (using a toothbrush, opening doors, using cell-phones, etc.), or in professional situations (medical interventions, manufacturing processes, etc.) we typically perform dexterous manipulations that involve multiple fingers and palm for both hands. Therefore, multi-Finger haptic methods can provide a realistic and natural human-machine interface to enhance immersion when interacting with simulated or remote environments. Most commercial devices allow haptic interaction with only one contact point, which may be sufficient for some exploration or palpation tasks but are not enough to perform advanced object manipulations such as grasping. In this thesis, I investigate the mechanical design, control and applications of a modular haptic device that can provide force feedback to the index, thumb and middle fingers of the user. The designed mechanical device is optimized with a multi-objective design function to achieve a low inertia, a large workspace, manipulability, and force-feedback of up to 3 N within the workspace; the bandwidth and rigidity for the device is assessed through simulation and real experimentation. One of the most important areas when designing haptic devices is the end-effector, since it is in contact with the user. In this thesis the design and evaluation of a thimble-like, lightweight, user-adaptable, and cost-effective device that incorporates four contact force sensors is described. This design allows estimation of the forces applied by a user during manipulation of virtual and real objects. The design of a real-time, modular control architecture for multi-finger haptic interaction is described. Requirements for control of multi-finger haptic devices are explored. Moreover, a large number of signals have to be acquired, processed, sent over the network and mathematical computations such as device direct and inverse kinematics, jacobian, grasp detection algorithms, etc. have to be calculated in Real Time to assure the required high fidelity for the haptic interaction. The Hardware control architecture has different modules and consists of an FPGA for the low-level controller and a RT controller for managing all the complex calculations (jacobian, kinematics, etc.); this provides a compact and scalable solution for the required high computation capabilities assuring a correct frequency rate for the control loop of 1 kHz. A set-up for dexterous virtual and real manipulation is described. Moreover, a new algorithm named the iterative kinematic decoupling method was implemented to solve the inverse kinematics of a robotic manipulator. In order to understand the importance of multi-modal interaction including haptics, a subject study was carried out to look for sensory stimuli that correlate with fast response time and enhanced accuracy. This experiment was carried out in collaboration with neuro-scientists from Technion Israel Institute of Technology. By comparing the grasping response times in unimodal (auditory, visual, and haptic) events with the response times in events with bimodal and trimodal combinations. It is concluded that in grasping tasks the synchronized motion of the fingers to generate the grasping response relies on haptic cues. This processing-speed advantage of haptic cues suggests that multimodalhaptic virtual environments are superior in generating motor contingencies, enhancing the plausibility of events. Applications that include haptics provide users with more time at the cognitive stages to fill in missing information creatively and form a richer experience. A major application of haptic devices is the design of new simulators to train manual skills for the medical sector. In collaboration with physical therapists from Griffith University in Australia, we developed a simulator to allow hand rehabilitation manipulations. First, the non-linear stiffness properties of the metacarpophalangeal joint of the index finger were estimated by using the designed end-effector; these parameters are implemented in a scenario that simulates the behavior of the human hand and that allows haptic interaction through the designed haptic device. The potential application of this work is related to educational and medical training purposes. In this thesis, new methods to simultaneously control the position and orientation of a robotic manipulator and the grasp of a robotic hand when interacting with large real environments are studied. The reachable workspace is extended by automatically switching between rate and position control modes. Moreover, the human hand gesture is recognized by reading the relative movements of the index, thumb and middle fingers of the user during the early stages of the approximation-to-the-object phase and then mapped to the robotic hand actuators. These methods are validated to perform dexterous manipulation of objects with a robotic manipulator, and different robotic hands. This work is the result of a research collaboration with researchers from the Harvard BioRobotics Laboratory. The developed experiments show that the overall task time is reduced and that the developed methods allow for full dexterity and correct completion of dexterous manipulations.

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.