Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

Fractional Calculus: Application in modeling and control

This contribution introduces the fractional calculus (FC) fundamental mathematical aspects and discuses some of their consequences. Based on the FC concepts, the chapter reviews the main approaches for implementing fractional operators and discusses the adoption of FC in control systems. Finally are presented some applications in the areas of modeling and control, namely fractional PID, heat diffusion systems, electromagnetism, fractional electrical impedances, evolutionary algorithms, robotics, and nonlinear system control.

Estado da Arte da Computação Evolutiva Aplicada à Eletrónica

A Computação Evolutiva enquadra-se na área da Inteligência Artificial e é um ramo das ciências da computação que tem vindo a ser aplicado na resolução de problemas em diversas áreas da Engenharia. Este trabalho apresenta o estado da arte da Computação Evolutiva, assim como algumas das suas aplicações no ramo da eletrónica, denominada Eletrónica Evolutiva (ou Hardware Evolutivo), enfatizando a síntese de circuitos digitais combinatórios. Em primeiro lugar apresenta-se a Inteligência Artificial, passando à Computação Evolutiva, nas suas principais vertentes: os Algoritmos Evolutivos baseados no processo da evolução das espécies de Charles Darwin e a Inteligência dos Enxames baseada no comportamento coletivo de alguns animais. No que diz respeito aos Algoritmos Evolutivos, descrevem-se as estratégias evolutivas, a programação genética, a programação evolutiva e com maior ênfase, os Algoritmos Genéticos. Em relação à Inteligência dos Enxames, descreve-se a otimização por colônia de formigas e a otimização por enxame de partículas. Em simultâneo realizou-se também um estudo da Eletrónica Evolutiva, explicando sucintamente algumas das áreas de aplicação, entre elas: a robótica, as FPGA, o roteamento de placas de circuito impresso, a síntese de circuitos digitais e analógicos, as telecomunicações e os controladores. A título de concretizar o estudo efetuado, apresenta-se um caso de estudo da aplicação dos algoritmos genéticos na síntese de circuitos digitais combinatórios, com base na análise e comparação de três referências de autores distintos. Com este estudo foi possível comparar, não só os resultados obtidos por cada um dos autores, mas também a forma como os algoritmos genéticos foram implementados, nomeadamente no que diz respeito aos parâmetros, operadores genéticos utilizados, função de avaliação, implementação em hardware e tipo de codificação do circuito.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Estudi comparatiu de l'algorisme basat en una colònia artificial d'abelles

La finalitat d'aquest projecte és la realització d'un estudi comparatiu de l'algoritme basat en una colònia artificial d'abelles, Artificial Bee Colony (ABC), comparat amb un conjunt d'algoritmes fonamentats en el paradigma de la computació evolutiva. S'utilitzarà l'eficàcia a l'hora d'optimitzar diverses funcions com a mesura comparativa. Els algoritmes amb els quals es comparara l'algoritme ABC són: algoritmes genètics, evolució diferencial i optimització amb eixam de partícules.

Estimating parameters for generalized mass action models using constraint propagation.

As modern molecular biology moves towards the analysis of biological systems as opposed to their individual components, the need for appropriate mathematical and computational techniques for understanding the dynamics and structure of such systems is becoming more pressing. For example, the modeling of biochemical systems using ordinary differential equations (ODEs) based on high-throughput, time-dense profiles is becoming more common-place, which is necessitating the development of improved techniques to estimate model parameters from such data. Due to the high dimensionality of this estimation problem, straight-forward optimization strategies rarely produce correct parameter values, and hence current methods tend to utilize genetic/evolutionary algorithms to perform non-linear parameter fitting. Here, we describe a completely deterministic approach, which is based on interval analysis. This allows us to examine entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In particular, we show how our method may be applied to a generic class of ODEs used for modeling biochemical systems called Generalized Mass Action Models (GMAs). In addition, we show that for GMAs our method is amenable to the technique in interval arithmetic called constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply it to some networks of biochemical reactions appearing in the literature, showing in particular that, in addition to estimating system parameters in the absence of noise, our method may also be used to recover the topology of these networks.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Similarity measure based classification

Luokittelujärjestelmää suunniteltaessa tarkoituksena on rakentaa systeemi, joka pystyy ratkaisemaan mahdollisimman tarkasti tutkittavan ongelma-alueen. Hahmontunnistuksessa tunnistusjärjestelmän ydin on luokitin. Luokittelun sovellusaluekenttä on varsin laaja. Luokitinta tarvitaan mm. hahmontunnistusjärjestelmissä, joista kuvankäsittely toimii hyvänä esimerkkinä. Myös lääketieteen parissa tarkkaa luokittelua tarvitaan paljon. Esimerkiksi potilaan oireiden diagnosointiin tarvitaan luokitin, joka pystyy mittaustuloksista päättelemään mahdollisimman tarkasti, onko potilaalla kyseinen oire vai ei. Väitöskirjassa on tehty similaarisuusmittoihin perustuva luokitin ja sen toimintaa on tarkasteltu mm. lääketieteen paristatulevilla data-aineistoilla, joissa luokittelutehtävänä on tunnistaa potilaan oireen laatu. Väitöskirjassa esitetyn luokittimen etuna on sen yksinkertainen rakenne, josta johtuen se on helppo tehdä sekä ymmärtää. Toinen etu on luokittimentarkkuus. Luokitin saadaan luokittelemaan useita eri ongelmia hyvin tarkasti. Tämä on tärkeää varsinkin lääketieteen parissa, missä jo pieni tarkkuuden parannus luokittelutuloksessa on erittäin tärkeää. Väitöskirjassa ontutkittu useita eri mittoja, joilla voidaan mitata samankaltaisuutta. Mitoille löytyy myös useita parametreja, joille voidaan etsiä juuri kyseiseen luokitteluongelmaan sopivat arvot. Tämä parametrien optimointi ongelma-alueeseen sopivaksi voidaan suorittaa mm. evoluutionääri- algoritmeja käyttäen. Kyseisessä työssä tähän on käytetty geneettistä algoritmia ja differentiaali-evoluutioalgoritmia. Luokittimen etuna on sen joustavuus. Ongelma-alueelle on helppo vaihtaa similaarisuusmitta, jos kyseinen mitta ei ole sopiva tutkittavaan ongelma-alueeseen. Myös eri mittojen parametrien optimointi voi parantaa tuloksia huomattavasti. Kun käytetään eri esikäsittelymenetelmiä ennen luokittelua, tuloksia pystytään parantamaan.

Normaalijakaumaan perustuva mutaatio-operaatio differentiaalievoluutioalgoritmiin

Evoluutioalgoritmit ovat viime vuosina osoittautuneet tehokkaiksi menetelmiksi globaalien optimointitehtävien ratkaisuun. Niiden vahvuutena on etenkin yleiskäyttöisyys ja kyky löytää globaali ratkaisu juuttumatta optimoitavan tavoitefunktion paikallisiin optimikohtiin. Tässä työssä on tavoitteena kehittää uusi, normaalijakaumaan perustuva mutaatio-operaatio differentiaalievoluutioalgoritmiin, joka on eräs uusimmista evoluutiopohjaisista optimointialgoritmeista. Menetelmän oletetaan vähentävän entisestään sekä populaation ennenaikaisen suppenemisen, että algoritmin tilojen juuttumisen riskiä ja se on teoreettisesti osoitettavissa suppenevaksi. Tämä ei päde alkuperäisen differentiaalievoluution tapauksessa, koska on voitu osoittaa, että sen tilanmuutokset voivat pienellä todennäköisyydellä juuttua. Työssä uuden menetelmän toimintaa tarkastellaan kokeellisesti käyttäen testiongelmina monirajoiteongelmia. Rajoitefunktioiden käsittelyyn käytetään Jouni Lampisen kehittämää, Pareto-optimaalisuuden periaatteeseen perustuvaa menetelmää. Samalla saadaan kerättyä lisää kokeellista näyttöä myös tämän menetelmän toiminnasta. Kaikki käytetyt testiongelmat kyettiin ratkaisemaan sekä alkuperäisellä differentiaalievoluutiolla, että uutta mutaatio-operaatiota käyttävällä versiolla. Uusi menetelmä osoittautui kuitenkin luotettavammaksi sellaisissa tapauksissa, joissa alkuperäisellä algoritmilla oli vaikeuksia. Lisäksi useimmat ongelmat kyettiin ratkaisemaan luotettavasti pienemmällä populaation koolla kuin alkuperäistä differentiaalievoluutiota käytettäessä. Uuden menetelmän käyttö myös mahdollistaa paremmin sellaisten kontrolliparametrien käytön, joilla hausta saadaan rotaatioinvariantti. Laskennallisesti uusi menetelmä on hieman alkuperäistä differentiaalievoluutiota raskaampi ja se tarvitsee yhden kontrolliparametrin enemmän. Uusille kontrolliparametreille määritettiin kuitenkin mahdollisimman yleiskäyttöiset arvot, joita käyttämällä on mahdollista ratkaista suuri joukko erilaisia ongelmia.

Populaation monimuotoisuuden mittaaminen liukulukukoodatuissa evoluutioalgoritmeissa

Diplomityössä esitetään menetelmä populaation monimuotoisuuden mittaamiseen liukulukukoodatuissa evoluutioalgoritmeissa, ja tarkastellaan kokeellisesti sen toimintaa. Evoluutioalgoritmit ovat populaatiopohjaisia menetelmiä, joilla pyritään ratkaisemaan optimointiongelmia. Evoluutioalgoritmeissa populaation monimuotoisuuden hallinta on välttämätöntä, jotta suoritettu haku olisi riittävän luotettavaa ja toisaalta riittävän nopeaa. Monimuotoisuuden mittaaminen on erityisen tarpeellista tutkittaessa evoluutioalgoritmien dynaamista käyttäytymistä. Työssä tarkastellaan haku- ja tavoitefunktioavaruuden monimuotoisuuden mittaamista. Toistaiseksi ei ole ollut olemassa täysin tyydyttäviä monimuotoisuuden mittareita, ja työn tavoitteena on kehittää yleiskäyttöinen menetelmä liukulukukoodattujen evoluutioalgoritmien suhteellisen ja absoluuttisen monimuotoisuuden mittaamiseen hakuavaruudessa. Kehitettyjen mittareiden toimintaa ja käyttökelpoisuutta tarkastellaan kokeellisesti ratkaisemalla optimointiongelmia differentiaalievoluutioalgoritmilla. Toteutettujen mittareiden toiminta perustuu keskihajontojen laskemiseen populaatiosta. Keskihajonnoille suoritetaan skaalaus, joko alkupopulaation tai nykyisen populaation suhteen, riippuen lasketaanko absoluuttista vai suhteellista monimuotoisuutta. Kokeellisessa tarkastelussa havaittiin kehitetyt mittarit toimiviksi ja käyttökelpoisiksi. Tavoitefunktion venyttäminen koordinaattiakseleiden suunnassa ei vaikuta mittarin toimintaan. Myöskään tavoitefunktion kiertäminen koordinaatistossa ei vaikuta mittareiden tuloksiin. Esitetyn menetelmän aikakompleksisuus riippuu lineaarisesti populaation koosta, ja mittarin toiminta on siten nopeaa suuriakin populaatioita käytettäessä. Suhteellinen monimuotoisuus antaa vertailukelpoisia tuloksia riippumatta parametrien lukumäärästä tai populaation koosta.

ContinuousMultimodal Global Optimization with Differential Evolution-Based Methods

Metaheuristic methods have become increasingly popular approaches in solving global optimization problems. From a practical viewpoint, it is often desirable to perform multimodal optimization which, enables the search of more than one optimal solution to the task at hand. Population-based metaheuristic methods offer a natural basis for multimodal optimization. The topic has received increasing interest especially in the evolutionary computation community. Several niching approaches have been suggested to allow multimodal optimization using evolutionary algorithms. Most global optimization approaches, including metaheuristics, contain global and local search phases. The requirement to locate several optima sets additional requirements for the design of algorithms to be effective in both respects in the context of multimodal optimization. In this thesis, several different multimodal optimization algorithms are studied in regard to how their implementation in the global and local search phases affect their performance in different problems. The study concentrates especially on variations of the Differential Evolution algorithm and their capabilities in multimodal optimization. To separate the global and local search search phases, three multimodal optimization algorithms are proposed, two of which hybridize the Differential Evolution with a local search method. As the theoretical background behind the operation of metaheuristics is not generally thoroughly understood, the research relies heavily on experimental studies in finding out the properties of different approaches. To achieve reliable experimental information, the experimental environment must be carefully chosen to contain appropriate and adequately varying problems. The available selection of multimodal test problems is, however, rather limited, and no general framework exists. As a part of this thesis, such a framework for generating tunable test functions for evaluating different methods of multimodal optimization experimentally is provided and used for testing the algorithms. The results demonstrate that an efficient local phase is essential for creating efficient multimodal optimization algorithms. Adding a suitable global phase has the potential to boost the performance significantly, but the weak local phase may invalidate the advantages gained from the global phase.

Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

Differential Evolution approach and parameter estimation of chaotic

Parameter estimation still remains a challenge in many important applications. There is a need to develop methods that utilize achievements in modern computational systems with growing capabilities. Owing to this fact different kinds of Evolutionary Algorithms are becoming an especially perspective field of research. The main aim of this thesis is to explore theoretical aspects of a specific type of Evolutionary Algorithms class, the Differential Evolution (DE) method, and implement this algorithm as codes capable to solve a large range of problems. Matlab, a numerical computing environment provided by MathWorks inc., has been utilized for this purpose. Our implementation empirically demonstrates the benefits of a stochastic optimizers with respect to deterministic optimizers in case of stochastic and chaotic problems. Furthermore, the advanced features of Differential Evolution are discussed as well as taken into account in the Matlab realization. Test "toycase" examples are presented in order to show advantages and disadvantages caused by additional aspects involved in extensions of the basic algorithm. Another aim of this paper is to apply the DE approach to the parameter estimation problem of the system exhibiting chaotic behavior, where the well-known Lorenz system with specific set of parameter values is taken as an example. Finally, the DE approach for estimation of chaotic dynamics is compared to the Ensemble prediction and parameter estimation system (EPPES) approach which was recently proposed as a possible solution for similar problems.

Modelling of potentiometric ion sensors

Potentiometric ion sensors are a very important subgroup of electrochemical sensors, very attractive for practical applications due to their small size, portability, low-energy consumption, relatively low cost and not changing the sample composition. They are investigated by the researchers from many fields of science. The continuous development of this field creates the necessity for a detailed description of sensor response and the electrochemical processes important in the practical applications of ion sensors. The aim of this thesis is to present the existing models available for the description of potentiometric ion sensors as well as their applicability and limitations. This includes the description of the diffusion potential occurring at the reference electrodes. The wide range of existing models, from most idealised phase boundary models to most general models, including migration, is discussed. This work concentrates on the advanced modelling of ion sensors, namely the Nernst-Planck-Poisson (NPP) model, which is the most general of the presented models, therefore the most widely applicable. It allows the modelling of the transport processes occurring in ion sensors and generating the potentiometric response. Details of the solution of the NPP model (including the numerical methods used) are shown. The comparisons between NPP and the more idealized models are presented. The applicability of the model to describe the formation of diffusion potential in reference electrode, the lower detection limit of both ion-exchanger and neutral carrier electrodes and the effect of the complexation in the membrane are discussed. The model was applied for the description of both types of electrodes, i.e. with the inner filling solution and solidcontact electrodes. The NPP model allows the electrochemical methods other than potentiometry to be described. Application of this model in Electrochemical Impedance Spectroscopy is discussed and a possible use in chrono-potentiometry is indicated. By combining the NPP model with evolutionary algorithms, namely Hierarchical Genetic Strategy (HGS), a novel method allowing the facilitation of the design of ion sensors was created. It is described in detail in this thesis and its possible applications in the field of ion sensors are indicated. Finally, some interesting effects occurring in the ion sensors (i.e. overshot response and influence of anionic sites) as well as the possible applications of NPP in biochemistry are described.