Blas Galván

Universidad de Las Palmas de Gran Canaria | ULPGC

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored bot...

Game-theory based Nash–evolutionary algorithms are efficient to speed-up and parallelize the optimum design procedure. They have been applied in several fields of engineering and sciences, mainly, in aeronautical and structural engineering. The influence of the search space player territory has been shown as having an important role in the algorith...

A general review of game-theory based evolutionary algorithms (EAs) is presented in this study. Nash equilibrium, Stackelberg game and Pareto optimality are considered, as game-theoretical basis of the evolutionary algorithm design, and also, as problems solved by evolutionary computation. Applications of game-theory based EAs in computational engi...

The use of evolutionary algorithms has been enhanced in recent years for solving real engineering problems, where the requirements of intense computational calculations are needed, especially when computational engineering simulations are involved (use of finite element method, boundary element method, etc). The coupling of game-theory concepts in...

En la actualidad, las empresas se manejan en un medio altamente competitivo, lo que les obliga a explorar nuevas técnicas para aumentar su productividad y reducir costos; una de las estrategias más comunes es la reducción de costos en actividades de mantenimiento. Tradicionalmente el mantenimiento de equipos sensibles como las bombas se ha venido r...

In this paper we deal with solving inverse problems in structural engineering (both the reconstruction inverse problem and the fully stressed design problem are considered). We apply a game-theory based Nash-evolutionary algorithm and compare it with the standard panmictic evolutionary algorithm. The procedure performance is analyzed on a ten bar s...

This paper reviews the combination of Artificial Neural Networks (ANN) and Evolutionary Optimisation (EO) to solve challenging problems for the academia and the industry. Both methodologies has been mixed in several ways in the last decade with more or less degree of success, but most of the contributions can be classified into the two following gr...

The optimization structural design problems of Fully Stressed Design (FSD) and Minimum Constrained Weight (MCW) are compared in this work in a simple truss test case with discrete cross-section type bar sizing, where both optimum designs are coincident. An analysis of the whole search space is included, and the optimization behaviour of evolutionar...

Oceanography was started by Navy engineers and the references of readiness and functional specifications were military. Since the end of the 80s, a new generation of instruments was able to promote more cost-efficient technical solutions. They cover now the needs of scientific ocean research as well as operational oceanography and environmental mon...

Debido al crecimiento de la demanda de electricidad y frente a los nuevos esquemas de calidad en el suministro del servicio eléctrico, el sector de generación eléctrica requiere una mejora constante de su infraestructura actual, ya sea por expansión con nuevas unidades o a través de mantenimientos oportunos donde se reemplazan componentes obsoletos...

This paper proposes a novel method to quantify the uncertainty inherent to the paths that ships will navigate in the future, from the information provided by the AIS system on the paths followed by the ships in the past. In the framework of the Dempster–Shafer theory, the proposed method analyzes the information contained in a known distribution of...

Lorsque l’on traite des problèmes d’optimisation réels de complexité NP-difficile et des objectifs à satisfaire contradictoires, les Algorithmes Évolutionnaires Multi-Objectif (AEMO) ont prouvé obtenir d’excellents résultats. Une classification récente des AEMO proposée par Branke, comprend une approche -AEMO sous préférences partielles- intermédia...

This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance-reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncert...

This paper is concerned with the influence of the codification type on the optimization of frame structures for constrained minimum weight, when using discrete variables: cross-section types corresponding to standard profiles. Three different codifications are compared: standard binary, standard reflected gray, and other binary codification with a...

An optimization methodology to evaluate simultaneously optimum designs of multiple slope height values with their corresponding factors of safety is proposed in this paper when considering the problem of slope stability. Limit equilibrium methods and evolutionary computation are used as analysis and optimization tools, respectively. The slip surfac...

Gran Canaria is one of the seven Canary Islands, located near Morocco Shore, this position offers a significant renewable energy potential to supply local and tourist population with clean energy. The island is rich in wind and solar resources; it has some wind farms with more of 4000 hours equivalent per year. It has an Energy Plan that provides f...

Considering uncertainties in engineering optimum design is often a requirement. Here, the use of the deterministic optimum design as the reference point in g-dominance is proposed. The multiobjective optimum robust design in a structural engineering test case where uncertainties in the external loads are taken into account is proposed as applicatio...

Structural frames robust optimum design under uncertain loads is handled simultaneously minimizing the constrained mass (adding structural mass and constraint average distribution), as well as the constraint violation distribution standard deviation, using the non-dominated sorting genetic algorithm NSGA-II. The consideration of external loads as r...

Platea 4D (Plataforma de Explotación y Análisis de Información Geotemporal) es un proyecto permanente de I+D+i consistente en una plataforma software de ayuda a la toma de decisiones y planificación para grandes emergencias tales como: sucesos contaminantes del medio natural, accidentes con sustancias peligrosas, fenómenos meteorológicos adversos,...

An optimization methodology for complex energy system introducing external factors based on the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) used for solving a real-world power systems multiobjective nonlinear optimization problem is presented. The thermoeconomic, technology and environment objectives are included in t...

Se presenta una metodología reciente para optimización de un sistema energético complejo introduciendo factores externos, basada en el desarrollo de fronteras Pareto ¿óptimas a partir de algoritmos evolutivos multiobjetivos (AEMO) empleados en la resolución de problemas energéticos reales de optimización no lineal. Se incluyen objetivos: termo-econ...

Every day many kilometres of European highways are blocked by traffic jams. Congestion on roads and at airports adds the EU's fuel bill with a corresponding rise in pollution levels. In short, our present patterns of transport growth are unsustainable. One way of easing road congestion is to develop the efficient end-to-end movement of goods usin...

In this work we propose a Flexible and Parallel Evolutionary Computation to solve a problem of Aeronautic interest, which is the non-linear full potential flow problem inside a nozzle. As the target to verify a numerical scheme for nodal points is equivalent to minimize a corresponding objective function, our methodology consists in using Evolution...

In this article we present a methodological framework entitled ‘Analysis of Uncertainty and Robustness in Evolutionary Optimization’ or AUREO for short. This methodology was developed as a diagnosis tool to analyze the characteristics of the decision-making problems to be solved with Multi-Objective Evolutionary Algorithms (MOEA) in order to: 1) de...

Multiobjective shape design of acoustic attenuation barriers is handled using a boundary element method modeling and evolutionary algorithms. Noise barriers are widely used for environmental protection near population nucleus in order to reduce the noise impact. The minimization of the acoustic pressure and the minimization of the cost of the barri...

The so called second generation of Multi-Objective Evolutionary Algorithms (MOEAs) like NSGA-II, are highly efficient and obtain Pareto optimal fronts characterized mainly by a wider spread and visually distributed fronts. The subjacent idea is to provide the decision-makers (DM) with the most representative set of alternatives in terms of objectiv...

A new integrated system with the capacity to help minimize the coastal environmental impacts when a pollutant load is discharged into the sea, in particular oil spills, is presented. The system, named SAMM, integrates simulation models (oil spills, dumping of sewage, brine, etc.) and risk assessment tools for decision-making in emergency situations...

A new integrated system with the capacity to help minimize coastal environmental impacts when a pollutant load is discharged into the sea (particularly oil spills) is presented. The system (SAMM) integrated simulation models (oil spills sewage dumping, brine, etc.) and risk assessment tools for decision making in emergency situations so as to minim...

En el presente trabajo se describen las metodologías necesarias para realizar la optimización desde criterios, tanto económicos como de confiabilidad, de sistemas industriales complejos. Se utiliza como ejemplo ilustrativo la optimización conjunta del diseño y la estrategia de man-tenimiento de un sistema de seguridad de una central nuclear. Las me...

The use of Multi-Ojective Evolutionary Algorithm (MOEA) methodologies, distinguished for its aptitude to obtain a representative Pareto optimal front, cannot always be the most appropriate. In fact, there exist multi-objective engineering problems that identify one feasible solution in the objective space known as Working Point (WP), not necessaril...

Without Abstract

Considering evolutionary multiobjective algorithms for improving single objective optimization problems is focused in this work on introducing the concept of helper objectives in a computational mechanics problem: the constrained mass minimization in real discrete frame bar structures optimum design. The number of different cross-section types of t...

The chapter presents a methodology that uses evolutionary algorithms to obtain fluid velocities for potential flows inside a nozzle. A genetic algorithm with real encoding which, with the technique of partial sampling of the solution nodes, presents a number of advantages over other evolutionary algorithms is proposed in the chapter. These include...

Controlled elitism in NSGAII is analysed, applied to a multiobjective frame optimisation problem with discrete modelling. Influence of various mutation rates is considered. A double minimization is handled: constrained weight and number of different cross section types. The comparative statistical results of the test case show a convergence study d...

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related eq...

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-l...

We present a methodology that attempts to help spill decision responders select appropriate response options to minimize coastal environmental impacts when a tanker leaks oil into the sea. For example, which direction should the tanker head for – towards the open sea or a coastal area where the oil slick can be concentrated and picked up more easil...

Considering evolutionary multiobjective algorithms for improving single objective optimization problems is focused in this work on introducing the concept of helper objectives in a computational mechanics problem: the constrained mass minimization in real discrete frame bar structures optimum design. The number of different cross-section types of t...

Structural design optimization has been applied, in many structural types, including trusses and frames, mainly oriented to the mass minimization. An additional consideration is the sensitivity of these obtained optimum designs to load variations. Striving after a null constraint violation, even under this possible load variation, simultaneously wi...

A comparative study of the use of Gray coding in multicriteria evolutionary optimisation is performed using the SPEA2 and NSGAII algorithms and applied to a frame structural optimisation problem. A double minimization is handled: constrained mass and number of different cross-section types. Influence of various mutation rates is considered. The com...

In this work the use of qualitative preferences for classifying and selecting MOEAs is introduced. The classical notions of the Analyst and the so called Prescriptive Analysis are introduced explicitly in EMO, identifying some difficulties in exploiting the results of the comparative studies performed by the current fashion. A methodology is develo...

Since it has currently became essential to design more efficient and robust alternative techniques to solve hard optimisation problems in industry or science, and of easy use for practitioners, here a new way of developing simple Artificial Intelligence based Evolutionary Algorithms will be introduced. Our evolutionary computational implementation...

This chapter proposes a solution to a state constrained optimal control problem with parallel implementations of an intelligent optimization system based on evolutionary self-learning named flexible evolution agent (FEA). The agent has a dynamic structure of operators (DSO), enlargement of the genetic code (EGC) of each candidate solution, and uses...

A new efficient algorithm is introduced to evaluate (non) coherent fault trees, obtaining exact lower & upper bounds on system unavailability, with a prespecified maximum error. The algorithm is based on the canonical normal form of the Boolean function, but overcomes the large number of terms needed, by using an intrinsic order criterion (IOC) to...

This paper attempts to present a summarized survey in the field of Parallel Evolutionary Algorithms (PEAs), trying to highlight the most relevent aspects in the design and implementation of each class of PEAs. A review of the most advanced research activities on this field is presented. New tools for new challenges of industry and society, which ar...

In this work new safety systems multiobjective optimum design methodologies are introduced and compared. Various multicriteria evolutionary algorithms are analysed (SPEA2, NSGAII and controlled elitist-NSGAII) and applied to a Containment Spray Injection System of a nuclear power plant. Influence of various mutation rates is considered. A double mi...

In this paper we solve a state constrained optimal control problem using an intelligent system based on evolutionary self-learning computation. We try to find the optimal locations of liquid waste dumping sites in offshore waters, so that it is verified that the concentration of a considered pollutant does not exceed a pre-established limitation, e...

In this paper an alternative design for the internal structure of Evolutionary Algorithms is presented and a first implementation of such structure is tested. In order to improve the internal structure architecture of Evolutionary Algorithms as well as to learn which the adequate values of parameters and/or structure of operators are , a review sho...

Resumen—En este trabajo se presenta, en el campo de la Computación Evolutiva, una metodología que pretende ser un primer paso hacia un nuevo enfoque a la hora de afrontar la resolución de problemas de optimización, al que hemos denominado Evolución Flexible (EF). El procedimiento propuesto posee la característica de no tener que elegir a priori ent...

Genetic algorithms have been used to solve a global sourcing problem, that is the allocation of production lots to different plants distributed all over the world in order to minimise production costs. Indeed some types of cars, like Fiat Palio and Siena, are called "world cars", as they are assembled in different sites in the world, often very far...

The unit commitment (UC) and Economic Dispatch (ED) are important part of the set of actions included in the concept of Power Systems Operation. In this paper we describe the application of Genetic Algoritms (GAs) to UC and ED problems using both binary and real coding, with increased efficiency because the use of Convergence Accelerators derived f...

El objetivo del trabajo es incrementar la eficiencia computacional por reducción del tratamiento de la función objetivo introduciendo un patrón en el esquema que se acerque a la solución deseada. Con ello se reduce el cálculo de la función objetivo que en casi todos los casos representa aproximadamente el 80% de computación. El cromosoma patrón es...

This chapter presents solution for a state constrained optimal control problem using an intelligent system based on evolutionary self-learning computation. The problem is solved under a parallel implementation linked to flexible evolution agent (FEA) using an Internet-based distributed computing environment. The aim of the FEA is to secure the high...

Resumen En este trabajo se muestra como se pueden usar algoritmos evolutivos multiobjetivo anidados para optimizar sistemas técnicos complejos considerando criterios de Confiabilidad. Los recientes avances en los algoritmos evolutivos multiobjetivo denominados de "Segunda Generación" han conseguido incrementar la eficiencia de la búsqueda a cotas s...

In this paper the use of a powerful single-objective optimization methodology in Multi-objective Optimization Algorithms (MOEAs) is introduced. The Flexible Evolution concepts (FE) have been recently developed and proved its efficiency gains compared with several Evolutionary Algorithms solving single-objective challenging problems. The main featur...

Since it has currently became essential to design more efficient and robust alternative procedures to optimize real industrial problems, our aim in this paper is to introduce a new way of developing better global optimization evolutionary methods. As a sample, we present what we have called Flexible Evolution (FE), which permits to all operators to...

En este artículo se expone la metodología seguida por la nueva norma RIAC-HDBK- 217Plus, elaborada por el RIAC (Reliability Information Analysis Center) para la predicción de fiabilidad de componentes, circuitos y sistemas electrónicos. Esta nueva norma, que está basada en la experiencia acumulada y publicada en mayo de 2006, pretende sustituir a l...