Allocation of protective devices in distribution circuits using nonlinear programming models and genetic algorithms

The optimized allocation of protective devices in strategic points of the circuit improves the quality of the energy supply and the system reliability index. This paper presents a nonlinear integer programming (NLIP) model with binary variables, to deal with the problem of protective device allocation in the main feeder and all branches of an overhead distribution circuit, to improve the reliability index and to provide customers with service of high quality and reliability. The constraints considered in the problem take into account technical and economical limitations, such as coordination problems of serial protective devices, available equipment, the importance of the feeder and the circuit topology. The use of genetic algorithms (GAs) is proposed to solve this problem, using a binary representation that does (1) or does not (0) show allocation of protective devices (reclosers, sectionalizers and fuses) in predefined points of the circuit. Results are presented for a real circuit (134 busses), with the possibility of protective device allocation in 29 points. Also the ability of the algorithm in finding good solutions while improving significantly the indicators of reliability is shown. (C) 2003 Elsevier B.V. All rights reserved.

Use of genetic algorithms and solvation potential to study peptides structure

In this work, genetic algorithms concepts along with a rotamer library for proteins side chains and implicit solvation potential are used to optimize the tertiary structure of peptides. We starting from the known PDB structure of its backbone which is kept fixed while the side chains allowed adopting the conformations present in the rotamer library. It was used rotamer library independent of backbone and a implicit solvation potential. The structure of Mastoporan-X was predicted using several force fields with a growing complexity; we started it with a field where the only present interaction was Lennard-Jones. We added the Coulombian term and we considered the solvation effects through a term proportional to the solvent accessible area. This paper present good and interesting results obtained using the potential with solvation term and rotamer library. Hence, the algorithm (called YODA) presented here can be a good tool to the prediction problem. (c) 2007 Elsevier B.V. All rights reserved.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimal trajectory and solution of the inverse kinematics of a robotic manipulator by genetic algorithms

This paper presents the generation of optimal trajectories by genetic algorithms (GA) for a planar robotic manipulator. The implemented GA considers a multi-objective function that minimizes the end-effector positioning error together with the joints angular displacement and it solves the inverse kinematics problem for the trajectory. Computer simulations results are presented to illustrate this implementation and show the efficiency of the used methodology producing soft trajectories with low computing cost. © 2011 Springer-Verlag Berlin Heidelberg.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

Application of genetic algorithms for the calibration of an air quality model and its validation using pollutant measures from the surroundings of an electric power plant

[EN]This work presents the calibration and validation of an air quality finite element model applied to emissions from a thermal power plant located in Gran Canaria. The calibration is performed using genetic algorithms. To calibrate and validate the model, the authors use empirical measures of pollutants concentrations from 4 stations located nearby the power plant; an hourly record per station during 3 days is available. Measures from 3 stations will be used to calibrate, while validation will use measures from the remaining station…

Multiobjective genetic algorithms applied to heat transfer problems

In the present work, the multi-objective optimization by genetic algorithms is investigated and applied to heat transfer problems. Firstly, the work aims to compare different reproduction processes employed by genetic algorithms and two new promising processes are suggested. Secondly, in this work two heat transfer problems are studied under the multi-objective point of view. Specifically, the two cases studied are the wavy fins and the corrugated wall channel. Both these cases have already been studied by a single objective optimizer. Therefore, this work aims to extend the previous works in a more comprehensive study.

Ottimizzazione delle reti di distribuzione idrica tramite algoritmi genetici multi-obiettivo

Water distribution networks optimization is a challenging problem due to the dimension and the complexity of these systems. Since the last half of the twentieth century this field has been investigated by many authors. Recently, to overcome discrete nature of variables and non linearity of equations, the research has been focused on the development of heuristic algorithms. This algorithms do not require continuity and linearity of the problem functions because they are linked to an external hydraulic simulator that solve equations of mass continuity and of energy conservation of the network. In this work, a NSGA-II (Non-dominating Sorting Genetic Algorithm) has been used. This is a heuristic multi-objective genetic algorithm based on the analogy of evolution in nature. Starting from an initial random set of solutions, called population, it evolves them towards a front of solutions that minimize, separately and contemporaneously, all the objectives. This can be very useful in practical problems where multiple and discordant goals are common. Usually, one of the main drawback of these algorithms is related to time consuming: being a stochastic research, a lot of solutions must be analized before good ones are found. Results of this thesis about the classical optimal design problem shows that is possible to improve results modifying the mathematical definition of objective functions and the survival criterion, inserting good solutions created by a Cellular Automata and using rules created by classifier algorithm (C4.5). This part has been tested using the version of NSGA-II supplied by Centre for Water Systems (University of Exeter, UK) in MATLAB® environment. Even if orientating the research can constrain the algorithm with the risk of not finding the optimal set of solutions, it can greatly improve the results. Subsequently, thanks to CINECA help, a version of NSGA-II has been implemented in C language and parallelized: results about the global parallelization show the speed up, while results about the island parallelization show that communication among islands can improve the optimization. Finally, some tests about the optimization of pump scheduling have been carried out. In this case, good results are found for a small network, while the solutions of a big problem are affected by the lack of constraints on the number of pump switches. Possible future research is about the insertion of further constraints and the evolution guide. In the end, the optimization of water distribution systems is still far from a definitive solution, but the improvement in this field can be very useful in reducing the solutions cost of practical problems, where the high number of variables makes their management very difficult from human point of view.

Comparative DNA sequence and methylation analyses of orthologous genes in humans and non-human primates: Genetic and epigenetic footprints of evolution

Welche genetische Unterschiede machen uns verschieden von unseren nächsten Verwandten, den Schimpansen, und andererseits so ähnlich zu den Schimpansen? Was wir untersuchen und auch verstehen wollen, ist die komplexe Beziehung zwischen den multiplen genetischen und epigenetischen Unterschieden, deren Interaktion mit diversen Umwelt- und Kulturfaktoren in den beobachteten phänotypischen Unterschieden resultieren. Um aufzuklären, ob chromosomale Rearrangements zur Divergenz zwischen Mensch und Schimpanse beigetragen haben und welche selektiven Kräfte ihre Evolution geprägt haben, habe ich die kodierenden Sequenzen von 2 Mb umfassenden, die perizentrischen Inversionsbruchpunkte flankierenden Regionen auf den Chromosomen 1, 4, 5, 9, 12, 17 und 18 untersucht. Als Kontrolle dienten dabei 4 Mb umfassende kollineare Regionen auf den rearrangierten Chromosomen, welche mindestens 10 Mb von den Bruchpunktregionen entfernt lagen. Dabei konnte ich in den Bruchpunkten flankierenden Regionen im Vergleich zu den Kontrollregionen keine höhere Proteinevolutionsrate feststellen. Meine Ergebnisse unterstützen nicht die chromosomale Speziationshypothese für Mensch und Schimpanse, da der Anteil der positiv selektierten Gene (5,1% in den Bruchpunkten flankierenden Regionen und 7% in den Kontrollregionen) in beiden Regionen ähnlich war. Durch den Vergleich der Anzahl der positiv und negativ selektierten Gene per Chromosom konnte ich feststellen, dass Chromosom 9 die meisten und Chromosom 5 die wenigsten positiv selektierten Gene in den Bruchpunkt flankierenden Regionen und Kontrollregionen enthalten. Die Anzahl der negativ selektierten Gene (68) war dabei viel höher als die Anzahl der positiv selektierten Gene (17). Eine bioinformatische Analyse von publizierten Microarray-Expressionsdaten (Affymetrix Chip U95 und U133v2) ergab 31 Gene, die zwischen Mensch und Schimpanse differentiell exprimiert sind. Durch Untersuchung des dN/dS-Verhältnisses dieser 31 Gene konnte ich 7 Gene als negativ selektiert und nur 1 Gen als positiv selektiert identifizieren. Dieser Befund steht im Einklang mit dem Konzept, dass Genexpressionslevel unter stabilisierender Selektion evolvieren. Die meisten positiv selektierten Gene spielen überdies eine Rolle bei der Fortpflanzung. Viele dieser Speziesunterschiede resultieren eher aus Änderungen in der Genregulation als aus strukturellen Änderungen der Genprodukte. Man nimmt an, dass die meisten Unterschiede in der Genregulation sich auf transkriptioneller Ebene manifestieren. Im Rahmen dieser Arbeit wurden die Unterschiede in der DNA-Methylierung zwischen Mensch und Schimpanse untersucht. Dazu wurden die Methylierungsmuster der Promotor-CpG-Inseln von 12 Genen im Cortex von Menschen und Schimpansen mittels klassischer Bisulfit-Sequenzierung und Bisulfit-Pyrosequenzierung analysiert. Die Kandidatengene wurden wegen ihrer differentiellen Expressionsmuster zwischen Mensch und Schimpanse sowie wegen Ihrer Assoziation mit menschlichen Krankheiten oder dem genomischen Imprinting ausgewählt. Mit Ausnahme einiger individueller Positionen zeigte die Mehrzahl der analysierten Gene keine hohe intra- oder interspezifische Variation der DNA-Methylierung zwischen den beiden Spezies. Nur bei einem Gen, CCRK, waren deutliche intraspezifische und interspezifische Unterschiede im Grad der DNA-Methylierung festzustellen. Die differentiell methylierten CpG-Positionen lagen innerhalb eines repetitiven Alu-Sg1-Elements. Die Untersuchung des CCRK-Gens liefert eine umfassende Analyse der intra- und interspezifischen Variabilität der DNA-Methylierung einer Alu-Insertion in eine regulatorische Region. Die beobachteten Speziesunterschiede deuten darauf hin, dass die Methylierungsmuster des CCRK-Gens wahrscheinlich in Adaption an spezifische Anforderungen zur Feinabstimmung der CCRK-Regulation unter positiver Selektion evolvieren. Der Promotor des CCRK-Gens ist anfällig für epigenetische Modifikationen durch DNA-Methylierung, welche zu komplexen Transkriptionsmustern führen können. Durch ihre genomische Mobilität, ihren hohen CpG-Anteil und ihren Einfluss auf die Genexpression sind Alu-Insertionen exzellente Kandidaten für die Förderung von Veränderungen während der Entwicklungsregulation von Primatengenen. Der Vergleich der intra- und interspezifischen Methylierung von spezifischen Alu-Insertionen in anderen Genen und Geweben stellt eine erfolgversprechende Strategie dar.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.