Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits

Big Data Optimization : Algorithmic Framework for Data Analysis Guided by Semantics

Fecha de Lectura de Tesis: 9 noviembre 2018.Over the past decade the rapid rise of creating data in all domains of knowledge such as traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the process of analyzing large data volumes, in order to be able to manage them with more easiness and in addition, discover new relationships which are hidden in them Optimization problems, which are commonly found in current industry, are not unrelated to this trend, therefore Multi-Objective Optimization Algorithms (MOA) should bear in mind this new scenario. This means that, MOAs have to deal with problems, which have either various data sources (typically streaming) of huge amount of data. Indeed these features, in particular, are found in Dynamic Multi-Objective Problems (DMOPs), which are related to Big Data optimization problems. Mostly with regards to velocity and variability. When dealing with DMOPs, whenever there exist changes in the environment that affect the solutions of the problem (i.e., the Pareto set, the Pareto front, or both), therefore in the fitness landscape, the optimization algorithm must react to adapt the search to the new features of the problem. Big Data analytics are long and complex processes therefore, with the aim of simplify them, a series of steps are carried out through. A typical analysis is composed of data collection, data manipulation, data analysis and finally result visualization. In the process of creating a Big Data workflow the analyst should bear in mind the semantics involving the problem domain knowledge and its data. Ontology is the standard way for describing the knowledge about a domain. As a global target of this PhD Thesis, we are interested in investigating the use of the semantic in the process of Big Data analysis, not only focused on machine learning analysis, but also in optimization

A new Taxonomy of Continuous Global Optimization Algorithms

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by design optimization. The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.Comment: 35 pages total, 28 written pages, 4 figures, 2019 Reworked Versio

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Abstract not availableH.R. Maier, Z. Kapelan, Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Ree

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

On the Application of Multiobjective Optimization to Software Development Process and Antenna Designing

Esta tesis doctoral, presentada como compendio de artículos, explora los beneficios prácticos del uso combinado de la optimización multi-objetivo con aplicaciones de simulación. En esta tesis, con un caracter de aplicación, se aportan ideas prácticas sobre cómo combinar meta-heurísticas aplicadas a la optimización de problemas con herramientas y técnicas de simulación. La simulación permite estudiar problemas complejos antes de implementarlos en el mundo real. Los problemas de optimización son de los más complicados de resolver. Involucran 3 o más variables y en muchos casos no pueden ser resueltos matemáticamente. La simulación permite modelar el problema, pero son una ayuda insuficiente a la hora de encontrar las mejores soluciones a dicho problema. En estos casos, el trabajo conjunto de la herramienta de simulación con metaheurísticas de optimización permiten abordar estos problemas con costes computacionales razonables, obteniendo resultados muy cercanos al óptimo. Debe tenerse en cuenta que las soluciones de los problemas multiobjetivo contienen un conjunto de variables donde habitualmente mejorar (optimizar) una variable, suponga empeorar (hacer menos óptima) otra(s). Por tanto, lo deseable es encontrar un conjunto de soluciones donde cada variable se optimiza teniendo en cuenta el posible impacto negativo en el resto de variables. A ese conjunto de soluciones, se le suele conocer como el Frente de Pareto Óptimo. Esta tesis presenta dos problemas reales, complejos y pertenecientes a campos totalmente distintos, que han sido resueltos de forma existosa, aplicando la misma técnica: Simulación combinada con optimización multiobjetivo. Esta tesis comienza presentando un caso de técnicas de optimización multiobjetivo a través de la simulación para ayudar a los directores de proyectos de software a encontrar las mejores configuraciones para los proyectos basados ITIL (Information Technology Infrastructure Library), de manera que se optimicen las estimaciones de calendario para un proyecto determinado, el tiempo y la productividad. Los datos de gestión de proyectos pueden obtenerse mediante simulación, por ejemplo, para optimizar el número de recursos utilizados en cada fase de la vida del proyecto. También se presenta otro caso de estudio sobre la forma en que la optimización de la simulación puede ayudar en el diseño de cualquier tipo de antena. En este caso de estudio, el objetivo es lograr una antena helicoidal, de doble banda, lo más compacta posible, para la telemetría, el seguimiento y el control (TTC) de los satélites. En los satélites es esencial reducir el volumen y el peso de los dispositivos instalados, manteniendo al mismo tiempo los requisitos de funcionamiento. Adicionalmente, esta tesis realiza un aporte teórico proponiendo un nuevo algoritmo llamado MNDS (Merge Non-Dominated Sorting) que mejora el rendimiento de los algoritmos de optimización multi-objectivo basados en el cálculo del Pareto Front

Metaheuristic models for decision support in the software construction process

En la actualidad, los ingenieros software no solo tienen la responsabilidad de construir sistemas que desempe~nen una determinada funcionalidad, sino que cada vez es más importante que dichos sistemas también cumplan con requisitos no funcionales como alta disponibilidad, efciencia o seguridad, entre otros. Para lograrlo, los ingenieros se enfrentan a un proceso continuo de decisión, pues deben estudiar las necesidades del sistema a desarrollar y las alternativas tecnológicas existentes para implementarlo. Todo este proceso debe estar encaminado a la obtención de sistemas software de gran calidad, reutilizables y que faciliten su mantenimiento y modificación en un escenario tan exigente y competitivo. La ingeniería del software, como método sistemático para la construcción de software, ha aportado una serie de pautas y tareas que, realizadas de forma disciplinada y adaptadas al contexto de desarrollo, posibilitan la obtención de software de calidad. En concreto, el proceso de análisis y diseño del software ha adquirido una gran importancia, pues en ella se concibe la estructura del sistema, en términos de sus bloques funcionales y las interacciones entre ellos. Es en este momento cuando se toman las decisiones acerca de la arquitectura, incluyendo los componentes que la conforman, que mejor se adapta a los requisitos, tanto funcionales como no funcionales, que presenta el sistema y que claramente repercuten en su posterior desarrollo. Por tanto, es necesario que el ingeniero analice rigurosamente las alternativas existentes, sus implicaciones en los criterios de calidad impuestos y la necesidad de establecer compromisos entre ellos. En este contexto, los ingenieros se guían principalmente por sus habilidades y experiencia, por lo que dotarles de métodos de apoyo a la decisión representaría un avance significativo en el área. La aplicación de técnicas de inteligencia artificial en este ámbito ha despertado un gran interés en los últimos años. En particular, la inteligencia artificial ha encontrado en la ingeniería del software un ámbito de aplicación complejo, donde diferentes técnicas pueden ayudar a conseguir la semi-automatización de tareas tradicionalmente realizadas de forma manual. De la unión de ambas áreas surge la denominada ingeniería del software basada en búsqueda, que propone la reformulación de las actividades propias de la ingeniería del software como problemas de optimización. A continuación, estos problemas podrían ser resueltos mediante técnicas de búsqueda como las metaheurísticas. Este tipo de técnicas se caracterizan por explorar el espacio de posibles soluciones de una manera \inteligente", a menudo simulando procesos naturales como es el caso de los algoritmos evolutivos. A pesar de ser un campo de investigación muy reciente, es posible encontrar propuestas para automatizar una gran variedad de tareas dentro del ciclo de vida del software, como son la priorización de requisitos, la planifcación de recursos, la refactorización del código fuente o la generación de casos de prueba. En el ámbito del análisis y diseño de software, cuyas tareas requieren de creatividad y experiencia, conseguir una automatización completa resulta poco realista. Es por ello por lo que la resolución de sus tareas mediante enfoques de búsqueda debe ser tratada desde la perspectiva del ingeniero, promoviendo incluso la interacción con ellos. Además, el alto grado de abstracción de algunas de sus tareas y la dificultad de evaluar cuantitativamente la calidad de un diseño software, suponen grandes retos en la aplicación de técnicas de búsqueda durante las fases tempranas del proceso de construcción de software. Esta tesis doctoral busca realizar aportaciones significativas al campo de la ingeniería del software basada en búsqueda y, más concretamente, al área de la optimización de arquitecturas software. Aunque se están realizando importantes avances en este área, la mayoría de propuestas se centran en la obtención de arquitecturas de bajo nivel o en la selección y despliegue de artefactos software ya desarrollados. Por tanto, no existen propuestas que aborden el modelado arquitectónico a un nivel de abstracción elevado, donde aún no existe un conocimiento profundo sobre cómo será el sistema y, por tanto, es más difícil asistir al ingeniero. Como problema de estudio, se ha abordado principalmente la tarea del descubrimiento de arquitecturas software basadas en componentes. El objetivo de este problema consiste en abstraer los bloques arquitectónicos que mejor definen la estructura actual del software, así como sus interacciones, con el fin de facilitar al ingeniero su posterior análisis y mejora. Durante el desarrollo de esta tesis doctoral se ha explorado el uso de una gran variedad de técnicas de búsqueda, estudiando su idoneidad y realizando las adaptaciones necesarias para hacer frente a los retos mencionados anteriormente. La primera propuesta se ha centrado en la formulación del descubrimiento de arquitecturas como problema de optimización, abordando la representación computacional de los artefactos software que deben ser modelados y definiendo medidas software para evaluar su calidad durante el proceso de búsqueda. Además, se ha desarrollado un primer modelo basado en algoritmos evolutivos mono-objetivo para su resolución, el cual ha sido validado experimentalmente con sistemas software reales. Dicho modelo se caracteriza por ser comprensible y exible, pues sus componentes han sido diseñados considerando estándares y herramientas del ámbito de la ingeniería del software, siendo además configurable en función de las necesidades del ingeniero. A continuación, el descubrimiento de arquitecturas ha sido tratado desde una perspectiva multiobjetivo, donde varias medidas software, a menudo en con icto, deben ser simultáneamente optimizadas. En este caso, la resolución del problema se ha llevado a cabo mediante ocho algoritmos del estado del arte, incluyendo propuestas recientes del ámbito de la optimización de muchos objetivos. Tras ser adaptados al problema, estos algoritmos han sido comparados mediante un extenso estudio experimental con el objetivo de analizar la ifnuencia que tiene el número y la elección de las métricas a la hora de guiar el proceso de búsqueda. Además de realizar una validación del rendimiento de estos algoritmos siguiendo las prácticas habituales del área, este estudio aporta un análisis detallado de las implicaciones que supone la optimización de múltiples objetivos en la obtención de modelos de soporte a la decisión. La última propuesta en el contexto del descubrimiento de arquitecturas software se centra en la incorporación de la opinión del ingeniero al proceso de búsqueda. Para ello se ha diseñado un mecanismo de interacción que permite al ingeniero indicar tanto las características deseables en las soluciones arquitectónicas (preferencias positivas) como aquellos aspectos que deben evitarse (preferencias negativas). Esta información es combinada con las medidas software utilizadas hasta el momento, permitiendo al algoritmo evolutivo adaptar la búsqueda conforme el ingeniero interactúe. Dadas las características del modelo, su validación se ha realizado con la participación de ingenieros con distinta experiencia en desarrollo software, a fin de demostrar la idoneidad y utilidad de la propuesta. En el transcurso de la tesis doctoral, los conocimientos adquiridos y las técnicas desarrolladas también han sido extrapolados a otros ámbitos de la ingeniería del software basada en búsqueda mediante colaboraciones con investigadores del área. Cabe destacar especialmente la formalización de una nueva disciplina transversal, denominada ingeniería del software basada en búsqueda interactiva, cuyo fin es promover la participación activa del ingeniero durante el proceso de búsqueda. Además, se ha explorado la aplicación de algoritmos de muchos objetivos a un problema clásico de la computación orientada a servicios, como es la composición de servicios web.Nowadays, software engineers have not only the responsibility of building systems that provide a particular functionality, but they also have to guarantee that these systems ful l demanding non-functional requirements like high availability, e ciency or security. To achieve this, software engineers face a continuous decision process, as they have to evaluate system needs and existing technological alternatives to implement it. All this process should be oriented towards obtaining high-quality and reusable systems, also making future modi cations and maintenance easier in such a competitive scenario. Software engineering, as a systematic method to build software, has provided a number of guidelines and tasks that, when done in a disciplinarily manner and properly adapted to the development context, allow the creation of high-quality software. More speci cally, software analysis and design has acquired great relevance, being the phase in which the software structure is conceived in terms of its functional blocks and their interactions. In this phase, engineers have to make decisions about the most suitable architecture, including its constituent components. Such decisions are made according to the system requirements, either functional or non-functional, and will have a great impact on its future development. Therefore, the engineer has to rigorously analyse existing alternatives, their implications on the imposed quality criteria and the need of establishing trade-o s among them. In this context, engineers are mostly guided by their own capabilities and experience, so providing them with decision support methods would represent a signi cant contribution. The application of arti cial intelligent techniques in this area has experienced a growing interest in the last years. Particularly, software engineering represents a complex application domain to arti cial intelligence, whose diverse techniques can help in the semi-automation of tasks traditionally performed manually. The union of both elds has led to the appearance of search-based software engineering, which proposes reformulating software engineering activities as optimisation problems. For their resolution, search techniques like metaheuristics can be then applied. This type of technique performs an \intelligent" exploration of the space of candidate solutions, often inspired by natural processes as happens with evolutionary algorithms. Despite the novelty of this research eld, there are proposals to automate a great variety of tasks within the software lifecycle, such as requirement prioritisation, resource planning, code refactoring or test case generation. Focusing on analysis and design, whose tasks require creativity and experience, trying to achieve full automation is not realistic. Therefore, solving design tasks by means of search approaches should be oriented towards the engineer's perspective, even promoting their interaction. Furthermore, design tasks are also characterised by a high level of abstraction and the di culty of quantitatively evaluating design quality. All these aspects represent key challenges for the application of search techniques in early phases of the software construction process. The aim of this Ph.D. Thesis is to make signi cant contributions in search-based software engineering and, specially, in the area of software architecture optimisation. Although it is an area in which signi cant progress is being done, most of the current proposals are focused on generating low-level architectures or selecting and deploying already developed artefacts. Therefore, there is a lack of proposals dealing with architectural modelling at a high level of abstraction. At this level, engineers do not have a deep understanding of the system yet, meaning that assisting them is even more di cult. As case study, the discovery of component-based software architectures has been primary addressed. The objective for this problem consists in the abstraction of the architectural blocks, and their interactions, that best de ne the current structure of a software system. This can be viewed as the rst step an engineer would perform in order to further analyse and improve the system architecture. In this Ph.D. Thesis, the use of a great variety of search techniques has been explored. The suitability of these techniques has been studied, also making the necessary adaptations to cope with the aforementioned challenges. A rst proposal has been focused on the formulation of software architecture discovery as an optimisation problem, which consists in the computational representation of its software artefacts and the de nition of software metrics to evaluate their quality during the search process. Moreover, a single-objective evolutionary algorithm has been designed for its resolution, which has been validated using real software systems. The resulting model is comprehensible and exible, since its components have been designed under software engineering standards and tools and are also con gurable according to engineer's needs. Next, the discovery of software architectures has been tackled from a multi-objective perspective, in which several software metrics, often in con ict, have to be simultaneously optimised. In this case, the problem is solved by applying eight state-of-theart algorithms, including some recent many-objective approaches. These algorithms have been adapted to the problem and compared in an extensive experimental study, whose purpose is to analyse the in uence of the number and combination of metrics when guiding the search process. Apart from the performance validation following usual practices within the eld, this study provides a detailed analysis of the practical implications behind the optimisation of multiple objectives in the context of decision support. The last proposal is focused on interactively including the engineer's opinion in the search-based architecture discovery process. To do this, an interaction mechanism has been designed, which allows the engineer to express desired characteristics for the solutions (positive preferences), as well as those aspects that should be avoided (negative preferences). The gathered information is combined with the software metrics used until the moment, thus making possible to adapt the search as the engineer interacts. Due to the characteristics of the proposed model, engineers of di erent expertise in software development have participated in its validation with the aim of showing the suitability and utility of the approach. The knowledge acquired along the development of the Thesis, as well as the proposed approaches, have also been transferred to other search-based software engineering areas as a result of research collaborations. In this sense, it is worth noting the formalisation of interactive search-based software engineering as a cross-cutting discipline, which aims at promoting the active participation of the engineer during the search process. Furthermore, the use of many-objective algorithms has been explored in the context of service-oriented computing to address the so-called web service composition problem