State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Using Optimality Theory and Reference Points to Improve the Diversity and Convergence of a Fuzzy-Adaptive Multi-Objective Particle Swarm Optimizer

Particle Swarm Optimization (PSO) has received increasing attention from the evolutionary optimization research community in the last twenty years. PSO is a metaheuristic approach based on collective intelligence obtained by emulating the swarming behavior of bees. A number of multi-objective variants of the original PSO algorithm that extend its applicability to optimization problems with conflicting objectives have also been developed; these multi-objective PSO (MOPSO) algorithms demonstrate comparable performance to other state-of-the-art metaheuristics. The existence of multiple optimal solutions (Pareto-optimal set) in optimization problems with conflicting objectives is not the only challenge posed to an optimizer, as the latter needs to be able to identify and preserve a well-distributed set of solutions during the search of the decision variable space. Recent attempts by evolutionary optimization researchers to incorporate mathematical convergence conditions into genetic algorithm optimizers have led to the derivation of a point-wise proximity measure, which is based on the solution of the achievement scalarizing function (ASF) optimization problem with a complementary slackness condition that quantifies the violation of the Karush-Kuhn-Tucker necessary conditions of optimality. In this work, the aforementioned KKT proximity measure is incorporated into the original Adaptive Coevolutionary Multi-Objective Swarm Optimizer (ACMOPSO) in order to monitor the convergence of the sub-swarms towards the Pareto-optimal front and provide feedback to Mamdani-type fuzzy logic controllers (FLCs) that are utilized for online adaptation of the algorithmic parameters. The proposed Fuzzy-Adaptive Multi-Objective Optimization Algorithm with the KKT proximity measure (FAMOPSOkkt) utilizes a set of reference points to cluster the computed nondominated solutions. These clusters interact with their corresponding sub-swarms to provide the swarm leaders and are also utilized to manage the external archive of nondominated solutions. The performance of the proposed algorithm is evaluated on benchmark problems chosen from the multi-objective optimization literature and compared to the performance of state-of-the-art multi-objective optimization algorithms with similar features

Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm

Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

© 2015 Qiang Long et al. Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust

Development of a hybrid algorithm for bi-level bi-objective optimization, and application to hydrogen supply chain deployment and design

The present master thesis is based on the recently presented doctoral thesis of Dr. Victor Hugo Cantu Medrano, addressing multiobjective optimization problems in Process Engineering with several alternative resolution methods using Evolutionary Computation. In his thesis, a new algorithm to find the optimal design of the Hydrogen Supply Chain while minimizing economic costs and environmental impact is presented. For its resolution, the algorithm divides the problem into two subproblems or levels. The first level deals with the design of the HSC structure (sizing and location of the facilities). A second level that solves the subproblem corresponding to the operation of the supply chain (production and transportation). The technique used for its resolution is a hybridization of the MOEA SMS-EMOA, for the first level, with a linear programming solver that uses a scalarization function to address the two objectives considered in the second level. In this line, this master thesis consists of developing an extension of this same algorithm with the objective of taking advantage of all the information generated in the second level to increase its efficiency. To achieve this, the second level is executed several times for each execution of the first level, using each time a different vector of weights in the scalarization function. But this new logic implies the readaptation of the whole algorithm. First, the Hydrogen Supply Chain problem is presented and the technique for solving the original algorithm is discussed. Subsequently, the necessary modifications to the MOEA are presented in order to be able to apply the new approach to the algorithm. With the new algorithm implemented, a study is carried out for the definition of the weight vectors and different scalarization functions are studied to try to increase its efficiency. Finally, the results obtained with the new algorithm and those of the original algorithm are compared to determine whether the new version is capable of solving the same problems using fewer computational resourcesCette thèse de master est basée sur la thèse de doctorat récemment soutenue par Dr Víctor Hugo Cantú Medrano, dans laquelle il expérimente plusieurs méthodes de résolution alternatives à l'aide méthodes évolutionnaires pour résoudre les problèmes d'optimisation multiobjectifs dans le domaine du génie des procédés. Dans sa thèse, le Dr Cantú présente un nouvel algorithme permettant de trouver la conception optimale de la chaîne d'approvisionnement en hydrogène tout en minimisant les coûts économiques et l'impact environnemental. Pour sa résolution, l'algorithme divise le problème en deux sous-problèmes ou niveaux. Le premier niveau traite de la conception de la structure de la chaîne logistique hydrogène (dimensionnement et emplacement des installations). Un second niveau résout le sous-problème correspondant à l'exploitation de la chaîne logistique (production et transport). La technique utilisée pour sa résolution est une hybridation du MOEA SMS-EMOA, pour le premier niveau, avec un solveur de programmation linéaire qui utilise une fonction de scalarisation pour traiter les deux objectifs considérés dans le second niveau. Dans cette lignée, ce mémoire de master consiste à développer une extension de ce même algorithme avec l'objectif de tirer profit de toute l'information générée dans le deuxième niveau pour augmenter son efficacité. Pour ce faire, le second niveau est exécuté plusieurs fois pour chaque exécution du premier niveau, en utilisant à chaque fois un vecteur de poids différent dans la fonction de scalarisation. Mais cette nouvelle logique implique la réadaptation de l'ensemble de l'algorithme. Tout d'abord, le problème de la chaîne logistique hydrogène est présenté et la technique de résolution de l'algorithme original est discutée. Ensuite, les modifications nécessaires au MEOA sont présentées afin de pouvoir appliquer la nouvelle approche à l'algorithme. Avec le nouvel algorithme implémenté, une étude est réalisée pour la définition des vecteurs de poids et différentes fonctions de scalarisation sont étudiées pour essayer d'augmenter son efficacité. Enfin, les résultats obtenus avec le nouvel algorithme et ceux de l'algorithme original sont comparés pour déterminer si la nouvelle version est capable de résoudre les mêmes problèmes en utilisant moins de ressources informatiquesEste Trabajo Final de Master parte de la tesis doctoral recientemente presentada del doctor Víctor Hugo Cantú Medrano, donde se abordan problemas de optimización multiobjetivo en Ingeniería de Procesos experimentando con varios métodos de resolución alternativos haciendo uso de la Computación Evolutiva. En su tesis, el doctor Cantú presenta un nuevo algoritmo para encontrar el diseño óptimo de la Hydrogen Supply Chain minimizando los costes económicos y el impacto ambiental. Para su resolución, el algoritmo divide el problema en dos subproblemas o niveles. Un primer nivel que aborda el diseño de la estructura de la HSC (dimensionamiento y ubicación de las instalaciones). Un segundo nivel que resuelve el subproblema correspondiente a la operación de la cadena de suministro (producción y transporte). La técnica empleada para su resolución es una hibridación del MOEA SMS-EMOA, para el primer nivel, con un solver de programación lineal que utiliza una función de escalarización para tratar los dos objetivos considerados en el segundo nivel. En esta línea, este trabajo consiste en desarrollar una extensión de este mismo algoritmo con el objetivo de aprovechar toda la información que se genera en el segundo nivel para aumentar su eficiencia. Para lograrlo se ejecuta varias veces el segundo nivel por cada ejecución del primer nivel, utilizando cada vez un vector de pesos diferente en la función de escalarización. Pero esta nueva lógica implica la readaptación de todo el algoritmo. En primer lugar, se presenta el problema de la Hydrogen Supply Chain y se discute la técnica de resolución del algoritmo original. Posteriormente se presentan las modificaciones necesarias en el MOEA para poder aplicar el nuevo enfoque al algoritmo. Ya con el nuevo algoritmo implementado se realiza un estudio para la definición de los vectores de peso y se estudian diferentes funciones de escalarización para tratar de aumentar su eficiencia. Por último, se comparan los resultados obtenidos con el nuevo algoritmo y los del original para determinar si la nueva versión es capaz de resolver los mismos problemas utilizando un menor número de recursos computacionalesAquest Treball Final de Màster té el seu origen en la tesis doctoral recentment presentada del doctor Víctor Hugo Cantú Medrano, en la qual s’aboren problemes d’optimització multiobjectiu en enginyeria de processos, experimentant amb diversos mètodes de resolució alternatius fent ús de la Computació Evolutiva. En la seva tesis, el doctor Cantú presenta un nou algorisme per a trobar el disseny òptim de la Hydrogen Supply Chain minimitzant els costos econòmics i l’impacte ambiental. Per a la seva resolució, l’algoritme divideix el problema en dos subproblemes o nivells. Un primer nivell aborda el disseny de l’estructura.de la HSC (dimensionament i ubicació de les instal·lacions). Un segon nivell resol el subproblema corresponent a l’operació de la cadena de subministrament (producció i transport). La tècnica empleada per a la seva resolució és una hibridació del MOEA SMS-EMOA, per al primer nivell amb un solver de programació lineal que utilitza una funció d’escalarització per a tractar els dos objectius considerats en el segon nivell. En aquesta línia, aquest treball consisteix a desenvolupar una extensió d’aquest mateix algorisme amb l’objectiu d’aprofitar tota la informació que es genera en el segon nivell per a augmentar la seva eficiència. Per a aconseguir-ho s’executa diverses vegades el segon nivell per cada execució del primer nivell, utilitzant cada vegada un vector de pesos diferent en la funció d’escalarització. Però aquesta nova lògica implica la readaptació de tot l’algorisme. En primer lloc, es presenta el problema de la Hydrogen Supply Chain i es discuteix la tècnica de resolució de l’algorisme original. Posteriorment es presenten les modificacions necessàries en el MOEA per a poder aplicar el nou enfocament a l’algorisme. Ja amb el nou algorisme implementat es realitza un estudi per a la definició dels vectors de pes i s’estudien diferents funcions d’escalarització per a tractar d’augmentar la seva eficiència. Ja amb el nou algorisme implementat es realitza un estudi per a la definició dels vectors de pes i s’estudien diferents funcions d’escalarització per a tractar d’augmentar la seva eficiència. Finalment, es comparen els resultats obtinguts amb el nou algorisme i els de l’original per tal de determinar si es possible obtenir els mateixos resultats fent us d’un menor número de recursos computacionalsObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètic

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use