Contributions to the development of the CRO-SL algorithm: Engineering applications problems

This Ph.D. thesis discusses advanced design issues of the evolutionary-based algorithm \textit{"Coral Reef Optimization"}, in its Substrate-Layer (CRO-SL) version, for optimization problems in Engineering Applications. The problems that can be tackled with meta-heuristic approaches is very wide and varied, and it is not exclusive of engineering. However we focus the Thesis on it area, one of the most prominent in our time. One of the proposed application is battery scheduling problem in Micro-Grids (MGs). Specifically, we consider an MG that includes renewable distributed generation and different loads, defined by its power profiles, and is equipped with an energy storage device (battery) to address its programming (duration of loading / discharging and occurrence) in a real scenario with variable electricity prices. Also, we discuss a problem of vibration cancellation over structures of two and four floors, using Tuned Mass Dampers (TMD's). The optimization algorithm will try to find the best solution by obtaining three physical parameters and the TMD location. As another related application, CRO-SL is used to design Multi-Input-Multi-Output Active Vibration Control (MIMO-AVC) via inertial-mass actuators, for structures subjected to human induced vibration. In this problem, we will optimize the location of each actuator and tune control gains. Finally, we tackle the optimization of a textile modified meander-line Inverted-F Antenna (IFA) with variable width and spacing meander, for RFID systems. Specifically, the CRO-SL is used to obtain an optimal antenna design, with a good bandwidth and radiation pattern, ideal for RFID readers. Radio Frequency Identification (RFID) has become one of the most numerous manufactured devices worldwide due to a reliable and inexpensive means of locating people. They are used in access and money cards and product labels and many other applications.Comment: arXiv admin note: text overlap with arXiv:1806.02654 by other author

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

Contributions to the development of the CRO-SL algorithm: Engineering applications problems

Esta tesis doctoral aborda el diseño del algoritmo evolutivo Coral Reef Optimization, en su versión Substrate-Layer, para la optimización de problemas en diferentes ámbitos de la ingeniería. Los algoritmos evolutivos han sido ampliamente aplicados a problemas de optimización difícilmente abordables de manera analítica, ya sea por tener espacios de búsqueda enormes o por ser no lineales. Si bien la ejecución de estos algoritmos no supone un gran coste computacional hoy en día, sí lo supone las funciones de coste que constantemente deben evaluar. La creciente capacidad de procesamiento en la tecnología le abre las puertas al abordaje de problemas temporalmente costosos por medio de la metaheurística. Uno de los inconvenientes de esta, es que no hay forma de saber a priori cuál de ellos es mejor para un problema específico, y sea cual sea la elección, la ejecución del mismo no te asegura que vayas a obtener el óptimo. Es por este motivo por el cual se ha elegido el algoritmo CRO-SL, ya que permite combinar los procesos de búsqueda más potentes, ayudándose entre ellos para alcanzar el óptimo global. La problemática a la que se puede aplicar la metaheurística es muy variada y no tiene por qué ser exclusiva de la ingeniería, sin embargo en esta tesis sí vamos a centrarla en ella. Una de las aplicaciones que vamos a ver es el diseño de una antena de tipo F invertida (IFA), para sistemas de IDentificación por Radio-Frecuencia (RFID). Estas han sido muy utilizadas en productos a lo largo de todo el mundo tanto en tarjetas de crédito como en etiquetas de productos debido a su pequeño tamaño y a una fabricación sencilla y barata. En concreto, en este trabajo se usarán como conductores láminas de cobre y como dieléctrico, fieltro. Se pretende así, diseñar el ancho y el espaciamiento de estas tiras de cobre para que emita en un ancho de banda determinado con una calidad determinada. También se abordará un problema de control de vibración en estructuras de dos y cuatro pisos mediante el uso de elementos amortiguadores pasivos, TMD's(Tunned Mass Dampers). Esta aplicación viene motivada por la necesidad de mitigar las vibraciones procedentes de la tierra, como pudiera ser en un terremoto. En este caso el algoritmo no sólo intentará optimizar las características físicas de los TMD's sino también su colocación dentro del edificio. En tercer lugar, se realizará un control activo de las vibraciones que generamos los humanos al caminar en una estructura civil, mediante el uso de actuadores de masa inercial. En este problema se tratará de optimizar la localización de los actuadores así como sintonizar las ganancias de control. Por último veremos un problema de optimización de planificación de las baterías en micro-redes(MG). Específicamente, consideramos una MG que incluye generación renovable y diferentes cargas, definidas por sus perfiles de potencia, y está equipada con un dispositivo de almacenamiento de energía (batería) para abordar su programación (duración de carga / descarga y ocurrencia) en un escenario real de precios variables de electricidad. Mediante la aplicación del CRO-SL a estos problemas se pretende cumplir dos objetivos. El primero es comprobar la aptitud del propio algoritmo en las aplicaciones mencionadas. Para ello además se realizarán experimentos con los algoritmos más populares y los resultados podrán ser comparados entre sí. El segundo es promover el uso del CRO-SL como herramienta de comparación entre métodos de exploración. Algunos de los algoritmos metaheurísticos se basan en la iteración de un proceso de búsqueda sobre una población de individuos codificados, que encarnan la solución a un determinado problema. El CRO-SL toma prestado la forma en la que otros algoritmos cambian a sus individuos, y forma nuevas soluciones de manera paralela. Entre los algoritmos evolutivos más conocidos que vamos a ver durante el desarrollo de esta tesis se encuentran los algoritmos Harmony Search, Differential Evolution y Genetic Algorithm. Además se verán otro tipo de mutaciones como la de tipo Gaussiana, mutación simple o cruce multipunto. Por último, durante el desarrollo de esta tesis también se ha probado una nueva forma de búsqueda basada en atractores extraños. Gracias a la capacidad de comparación del CRO-SL podremos ver si esta nueva forma de búsqueda es útil o no

Cross-entropy boosted CRO-SL for optimal power flow in smart grids

Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal is to determine the optimal operational parameters of a power-related system (in many cases a type of smart or micro grid) which guarantee an economic and effective power dispatch. In recent years, a number of approaches based on metaheuristics algorithms have been proposed to solve OPF problems. In this paper, we propose the use of the Cross-Entropy (CE) method as a first step depth search operator to assist population-based evolutionary methods in the framework of an OPF problem. Specifically, a new variant of the Coral Reefs Optimization with Substrate Layers algorithm boosted with CE method (CE+CRO-SL) is presented in this work. We have adopted the IEEE 57-Bus System as a test scenario which, by default, has seven thermal generators for power production for the grid. We have modified this system by replacing three thermal generators with renewable source generators, in order to consider a smart grid approach with renewable energy production. The performance of CE+CRO-SL in this particular case study scenario has been compared with that of well-known techniques such as population’s methods CMA-ES and EPSO (both boosted with CE). The results obtained indicate that CE+CRO-SL showed a superior performance than the alternative techniques in terms of efficiency and accuracy. This is justified by its greater exploration capacity, since it has internally operations coming from different heuristics, thus surpassing the performance of classic methods. Moreover, in a projection analysis, the CE+CRO-SL provides a profit of millions of dollars per month in all cases tested considering the modified version of the IEEE 57-Bus smart grid system

Eliminating stick-slip vibrations in drill-strings with a dual-loop control strategy optimized by the CRO-SL algorithm

Funding: This work was partially supported by the Spanish Ministerial Commission of Science and Technology (MICYT) through project number TIN2017-85887-C2-2-P Acknowledgments: The authors would like to thank Marian Wiercigroch and Vahid Vaziri from the Centre for Applied Dynamics Research, University of Aberdeen, for providing the realistic drill-string parameters used in this work.Peer reviewedPublisher PD

Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

A novel Island Model based on Coral Reefs Optimization algorithm for solving the unequal area facility layout problem

This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu