Optimal Thermal Distribution by using Inverse Genetic Algorithm Optimization Technique

Optimal arrangement of components on printed circuit board (PCB) has become a basic necessity so as to have effective management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve this objective. This paper proposes IGA search engine to optimize the thermal profile of components based on thermal resistance network and to minimize the area of PCB. Comparison between the proposed IGA and the conventional GA (FGA) performances are extensively analyzed. Unlike the conventional FGA, the IGA approach allows the user to set the desired fitness, so that the GA process will try to approach these set values. A reduction in the overall computational time and the freedom of choosing a desired fitness are the major advantages of IGA over FGA. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The computational time has also been minimized by 15.56%

Thermal and area optimization for component placement on PCB design using inverse genetic algorithm

Considering the current trend of compact designs which are mostly multiobjective in nature, proper arrangement of components has become a basic necessity so as to have optimal management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve optimal placement of components on printed circuit board (PCB). The objective functions are the PCB area and temperature of each component while the constraint parameters are; to avoid the overlapping of components, the maximum allowable PCB area is 2(120193.4)mm2 , thermal connections were internally set, and the manufacturer allowable temperature for the ICs must be more than the components optimal temperature. In the conventional Forward Genetic Algorithm (FGA) optimization, the individual fitness of components are generated through the GA process. The IGA approach on the other hand, allows the user to set the desired fitness, so that the GA process will try to approach these set values. Hence, the IGA has two major advantages over FGA; the first being a reduction in the overall computational time and the other is the freedom of choosing the desired fitness (i.e. ability to manipulate the GA output). The objectives of this work includes; development of an IGA search Engine, minimization of the thermal profile of components based on thermal resistance network and the area of PCB, and comparison of the proposed IGA and FGA performances. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The CPU-time has also been minimised by 15.56%

Towards an Information Theoretic Framework for Evolutionary Learning

The vital essence of evolutionary learning consists of information flows between the environment and the entities differentially surviving and reproducing therein. Gain or loss of information in individuals and populations due to evolutionary steps should be considered in evolutionary algorithm theory and practice. Information theory has rarely been applied to evolutionary computation - a lacuna that this dissertation addresses, with an emphasis on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity. We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price\u27s Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Parallel optimization algorithms for high performance computing : application to thermal systems

The need of optimization is present in every field of engineering. Moreover, applications requiring a multidisciplinary approach in order to make a step forward are increasing. This leads to the need of solving complex optimization problems that exceed the capacity of human brain or intuition. A standard way of proceeding is to use evolutionary algorithms, among which genetic algorithms hold a prominent place. These are characterized by their robustness and versatility, as well as their high computational cost and low convergence speed. Many optimization packages are available under free software licenses and are representative of the current state of the art in optimization technology. However, the ability of optimization algorithms to adapt to massively parallel computers reaching satisfactory efficiency levels is still an open issue. Even packages suited for multilevel parallelism encounter difficulties when dealing with objective functions involving long and variable simulation times. This variability is common in Computational Fluid Dynamics and Heat Transfer (CFD & HT), nonlinear mechanics, etc. and is nowadays a dominant concern for large scale applications. Current research in improving the performance of evolutionary algorithms is mainly focused on developing new search algorithms. Nevertheless, there is a vast knowledge of sequential well-performing algorithmic suitable for being implemented in parallel computers. The gap to be covered is efficient parallelization. Moreover, advances in the research of both new search algorithms and efficient parallelization are additive, so that the enhancement of current state of the art optimization software can be accelerated if both fronts are tackled simultaneously. The motivation of this Doctoral Thesis is to make a step forward towards the successful integration of Optimization and High Performance Computing capabilities, which has the potential to boost technological development by providing better designs, shortening product development times and minimizing the required resources. After conducting a thorough state of the art study of the mathematical optimization techniques available to date, a generic mathematical optimization tool has been developed putting a special focus on the application of the library to the field of Computational Fluid Dynamics and Heat Transfer (CFD & HT). Then the main shortcomings of the standard parallelization strategies available for genetic algorithms and similar population-based optimization methods have been analyzed. Computational load imbalance has been identified to be the key point causing the degradation of the optimization algorithm¿s scalability (i.e. parallel efficiency) in case the average makespan of the batch of individuals is greater than the average time required by the optimizer for performing inter-processor communications. It occurs because processors are often unable to finish the evaluation of their queue of individuals simultaneously and need to be synchronized before the next batch of individuals is created. Consequently, the computational load imbalance is translated into idle time in some processors. Several load balancing algorithms have been proposed and exhaustively tested, being extendable to any other population-based optimization method that needs to synchronize all processors after the evaluation of each batch of individuals. Finally, a real-world engineering application that consists on optimizing the refrigeration system of a power electronic device has been presented as an illustrative example in which the use of the proposed load balancing algorithms is able to reduce the simulation time required by the optimization tool.El aumento de las aplicaciones que requieren de una aproximación multidisciplinar para poder avanzar se constata en todos los campos de la ingeniería, lo cual conlleva la necesidad de resolver problemas de optimización complejos que exceden la capacidad del cerebro humano o de la intuición. En estos casos es habitual el uso de algoritmos evolutivos, principalmente de los algoritmos genéticos, caracterizados por su robustez y versatilidad, así como por su gran coste computacional y baja velocidad de convergencia. La multitud de paquetes de optimización disponibles con licencias de software libre representan el estado del arte actual en tecnología de optimización. Sin embargo, la capacidad de adaptación de los algoritmos de optimización a ordenadores masivamente paralelos alcanzando niveles de eficiencia satisfactorios es todavía una tarea pendiente. Incluso los paquetes adaptados al paralelismo multinivel tienen dificultades para gestionar funciones objetivo que requieren de tiempos de simulación largos y variables. Esta variabilidad es común en la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT), mecánica no lineal, etc. y es una de las principales preocupaciones en aplicaciones a gran escala a día de hoy. La investigación actual que tiene por objetivo la mejora del rendimiento de los algoritmos evolutivos está enfocada principalmente al desarrollo de nuevos algoritmos de búsqueda. Sin embargo, ya se conoce una gran variedad de algoritmos secuenciales apropiados para su implementación en ordenadores paralelos. La tarea pendiente es conseguir una paralelización eficiente. Además, los avances en la investigación de nuevos algoritmos de búsqueda y la paralelización son aditivos, por lo que el proceso de mejora del software de optimización actual se verá incrementada si se atacan ambos frentes simultáneamente. La motivación de esta Tesis Doctoral es avanzar hacia una integración completa de las capacidades de Optimización y Computación de Alto Rendimiento para así impulsar el desarrollo tecnológico proporcionando mejores diseños, acortando los tiempos de desarrollo del producto y minimizando los recursos necesarios. Tras un exhaustivo estudio del estado del arte de las técnicas de optimización matemática disponibles a día de hoy, se ha diseñado una librería de optimización orientada al campo de la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT). A continuación se han analizado las principales limitaciones de las estrategias de paralelización disponibles para algoritmos genéticos y otros métodos de optimización basados en poblaciones. En el caso en que el tiempo de evaluación medio de la tanda de individuos sea mayor que el tiempo medio que necesita el optimizador para llevar a cabo comunicaciones entre procesadores, se ha detectado que la causa principal de la degradación de la escalabilidad o eficiencia paralela del algoritmo de optimización es el desequilibrio de la carga computacional. El motivo es que a menudo los procesadores no terminan de evaluar su cola de individuos simultáneamente y deben sincronizarse antes de que se cree la siguiente tanda de individuos. Por consiguiente, el desequilibrio de la carga computacional se convierte en tiempo de inactividad en algunos procesadores. Se han propuesto y testado exhaustivamente varios algoritmos de equilibrado de carga aplicables a cualquier método de optimización basado en una población que necesite sincronizar los procesadores tras cada tanda de evaluaciones. Finalmente, se ha presentado como ejemplo ilustrativo un caso real de ingeniería que consiste en optimizar el sistema de refrigeración de un dispositivo de electrónica de potencia. En él queda demostrado que el uso de los algoritmos de equilibrado de carga computacional propuestos es capaz de reducir el tiempo de simulación que necesita la herramienta de optimización

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Quantification d’incertitude sur fronts de Pareto et stratégies pour l’optimisation bayésienne en grande dimension, avec applications en conception automobile

This dissertation deals with optimizing expensive or time-consuming black-box functionsto obtain the set of all optimal compromise solutions, i.e. the Pareto front. In automotivedesign, the evaluation budget is severely limited by numerical simulation times of the considered physical phenomena. In this context, it is common to resort to “metamodels” (models of models) of the numerical simulators, especially using Gaussian processes. They enable adding sequentially new observations while balancing local search and exploration. Complementing existing multi-objective Expected Improvement criteria, we propose to estimate the position of the whole Pareto front along with a quantification of the associated uncertainty, from conditional simulations of Gaussian processes. A second contribution addresses this problem from a different angle, using copulas to model the multi-variate cumulative distribution function. To cope with a possibly high number of variables, we adopt the REMBO algorithm. From a randomly selected direction, defined by a matrix, it allows a fast optimization when only a few number of variables are actually influential, but unknown. Several improvements are proposed, such as a dedicated covariance kernel, a selection procedure for the low dimensional domain and of the random directions, as well as an extension to the multi-objective setup. Finally, an industrial application in car crash-worthiness demonstrates significant benefits in terms of performance and number of simulations required. It has also been used to test the R package GPareto developed during this thesis.Cette thèse traite de l’optimisation multiobjectif de fonctions coûteuses, aboutissant à laconstruction d’un front de Pareto représentant l’ensemble des compromis optimaux. En conception automobile, le budget d’évaluations est fortement limité par les temps de simulation numérique des phénomènes physiques considérés. Dans ce contexte, il est courant d’avoir recours à des « métamodèles » (ou modèles de modèles) des simulateurs numériques, en se basant notamment sur des processus gaussiens. Ils permettent d’ajouter séquentiellement des observations en conciliant recherche locale et exploration. En complément des critères d’optimisation existants tels que des versions multiobjectifs du critère d’amélioration espérée, nous proposons d’estimer la position de l’ensemble du front de Pareto avec une quantification de l’incertitude associée, à partir de simulations conditionnelles de processus gaussiens. Une deuxième contribution reprend ce problème à partir de copules. Pour pouvoir traiter le cas d’un grand nombre de variables d’entrées, nous nous basons sur l’algorithme REMBO. Par un tirage aléatoire directionnel, défini par une matrice, il permet de trouver un optimum rapidement lorsque seules quelques variables sont réellement influentes (mais inconnues). Plusieurs améliorations sont proposées, elles comprennent un noyau de covariance dédié, une sélection du domaine de petite dimension et des directions aléatoires mais aussi l’extension au casmultiobjectif. Enfin, un cas d’application industriel en crash a permis d’obtenir des gainssignificatifs en performance et en nombre de calculs requis, ainsi que de tester le package R GPareto développé dans le cadre de cette thèse

Contributions `a la r´esolution de probl`emes d’optimisation combinatoires NP-difficiles

Cette th�ese porte sur des algorithmes e�caces pour la r�esolution de probl�emes d'optimisation combinatoires NP-di�ciles, avec deux contributions. La premi�ere contribution consiste en la proposition d'un nouvel algorithme multiob- jectif hybride combinant un algorithme g�en�etique avec un op�erateur de recherche bas�e sur l'optimisation par essaims de particules. L'objectif de cette hybridation est de surmonter les situations de convergence lente des algorithmes g�en�etiques multiobjectifs lors de la r�e- solution de probl�emes di�ciles �a plus de deux objectifs. Dans le sch�ema hybride propos�e, un algorithme g�en�etique multiobjectif Pareto applique p�eriodiquement un algorithme d'op- timisation par essaim de particules pour optimiser une fonction d'adaptation scalaire sur une population archive. Deux variantes de cet algorithme hybride sont propos�ees et adap- t�ees pour la r�esolution du probl�eme du sac �a dos multiobjectif. Les r�esultats exp�erimentaux prouvent que les algorithmes hybrides sont plus performants que les algorithmes standards. La seconde contribution concerne l'am�elioration d'un algorithme heuristique de recherche locale dit PALS (pour l'anglais Problem Aware Local Search) sp�eci�que au probl�eme d'as- semblage de fragments d'ADN, un probl�eme d'optimisation combinatoire NP-di�cile en bio-informatique des s�equences. Deux modi�cations �a PALS sont propos�ees. La premi�ere modi�cation permet d'�eviter les ph�enom�enes de convergence pr�ematur�ee vers des optima lo- caux. La seconde modi�cation conduit �a une r�eduction signi�cative des temps de calcul tout en conservant la pr�ecision des r�esultats. Apr�es des exp�erimentations r�ealis�ees sur les jeux de donn�ees disponibles dans la litt�erature, nos nouvelles variantes de PALS se r�ev�elent tr�es comp�etitives par rapport aux variantes existantes et �a d'autres algorithmes d'assemblage

Construction Requirements Driven Planning and Scheduling

Ph.DDOCTOR OF PHILOSOPH