972 resultados para Evolutionary multiobjective optimization
Resumo:
Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities.
Resumo:
This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.
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The higher education system in Europe is currently under stress and the debates over its reform and future are gaining momentum. Now that, for most countries, we are in a time for change, in the overall society and the whole education system, the legal and political dimensions have gained prominence, which has not been followed by a more integrative approach of the problem of order, its reform and the issue of regulation, beyond the typical static and classical cost-benefit analyses. The two classical approaches for studying (and for designing the policy measures of) the problem of the reform of the higher education system - the cost-benefit analysis and the legal scholarship description - have to be integrated. This is the argument of our paper that the very integration of economic and legal approaches, what Warren Samuels called the legal-economic nexus, is meaningful and necessary, especially if we want to address the problem of order (as formulated by Joseph Spengler) and the overall regulation of the system. On the one hand, and without neglecting the interest and insights gained from the cost-benefit analysis, or other approaches of value for money assessment, we will focus our study on the legal, social and political aspects of the regulation of the higher education system and its reform in Portugal. On the other hand, the economic and financial problems have to be taken into account, but in a more inclusive way with regard to the indirect and other socio-economic costs not contemplated in traditional or standard assessments of policies for the tertiary education sector. In the first section of the paper, we will discuss the theoretical and conceptual underpinning of our analysis, focusing on the evolutionary approach, the role of critical institutions, the legal-economic nexus and the problem of order. All these elements are related to the institutional tradition, from Veblen and Commons to Spengler and Samuels. The second section states the problem of regulation in the higher education system and the issue of policy formulation for tackling the problem. The current situation is clearly one of crisis with the expansion of the cohorts of young students coming to an end and the recurrent scandals in private institutions. In the last decade, after a protracted period of extension or expansion of the system, i. e., the continuous growth of students, universities and other institutions are competing harder to gain students and have seen their financial situation at risk. It seems that we are entering a period of radical uncertainty, higher competition and a new configuration that is slowly building up is the growth in intensity, which means upgrading the quality of the higher learning and getting more involvement in vocational training and life-long learning. With this change, and along with other deep ones in the Portuguese society and economy, the current regulation has shown signs of maladjustment. The third section consists of our conclusions on the current issue of regulation and policy challenge. First, we underline the importance of an evolutionary approach to a process of change that is essentially dynamic. A special attention will be given to the issues related to an evolutionary construe of policy analysis and formulation. Second, the integration of law and economics, through the notion of legal economic nexus, allows us to better define the issues of regulation and the concrete problems that the universities are facing. One aspect is the instability of the political measures regarding the public administration and on which the higher education system depends financially, legally and institutionally, to say the least. A corollary is the lack of clear strategy in the policy reforms. Third, our research criticizes several studies, such as the one made by the OECD in late 2006 for the Ministry of Science, Technology and Higher Education, for being too static and neglecting fundamental aspects of regulation such as the logic of actors, groups and organizations who are major players in the system. Finally, simply changing the legal rules will not necessary per se change the behaviors that the authorities want to change. By this, we mean that it is not only remiss of the policy maker to ignore some of the critical issues of regulation, namely the continuous non-respect by academic management and administrative bodies of universities of the legal rules that were once promulgated. Changing the rules does not change the problem, especially without the necessary debates form the different relevant quarters that make up the higher education system. The issues of social interaction remain as intact. Our treatment of the matter will be organized in the following way. In the first section, the theoretical principles are developed in order to be able to study more adequately the higher education transformation with a modest evolutionary theory and a legal and economic nexus of the interactions of the system and the policy challenges. After describing, in the second section, the recent evolution and current working of the higher education in Portugal, we will analyze the legal framework and the current regulatory practices and problems in light of the theoretical framework adopted. We will end with some conclusions on the current problems of regulation and the policy measures that are discusses in recent years.
CIDER - envisaging a COTS communication infrastructure for evolutionary dependable real-time systems
Resumo:
It is foreseen that future dependable real-time systems will also have to meet flexibility, adaptability and reconfigurability requirements. Considering the distributed nature of these computing systems, a communication infrastructure that permits to fulfil all those requirements is thus of major importance. Although Ethernet has been used primarily as an information network, there is a strong belief that some very recent technological advances will enable its use in dependable applications with real-time requirements. Indeed, several recently standardised mechanisms associated with Switched-Ethernet seem to be promising to enable communication infrastructures to support hard real-time, reliability and flexible distributed applications. This paper describes the motivation and the work being developed within the CIDER (Communication Infrastructure for Dependable Evolvable Real-Time Systems) project, which envisages the use of COTS Ethernet as an enabling technology for future dependable real-time systems. It is foreseen that the CIDER approach will constitute a relevant stream of research since it will bring together cutting edge research in the field of real-time and dependable distributed systems and the industrial eagerness to expand Ethernet responsabilities to support dependable real-time applications.
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It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.
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This paper proposes a stochastic mixed-integer linear approach to deal with a short-term unit commitment problem with uncertainty on a deregulated electricity market that includes day-ahead bidding and bilateral contracts. The proposed approach considers the typically operation constraints on the thermal units and a spinning reserve. The uncertainty is due to the electricity prices, which are modeled by a scenario set, allowing an acceptable computation. Moreover, emission allowances are considered in a manner to allow for the consideration of environmental constraints. A case study to illustrate the usefulness of the proposed approach is presented and an assessment of the cost for the spinning reserve is obtained by a comparison between the situation with and without spinning reserve.
Resumo:
Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.
Resumo:
Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.
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This paper studies the optimization of complex-order algorithms for the discrete-time control of linear and nonlinear systems. The fundamentals of fractional systems and genetic algorithms are introduced. Based on these concepts, complexorder control schemes and their implementation are evaluated in the perspective of evolutionary optimization. The results demonstrate not only that complex-order derivatives constitute a valuable alternative for deriving control algorithms, but also the feasibility of the adopted optimization strategy.
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The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.
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One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed. Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO.
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An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.
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In distributed soft real-time systems, maximizing the aggregate quality-of-service (QoS) is a typical system-wide goal, and addressing the problem through distributed optimization is challenging. Subtasks are subject to unpredictable failures in many practical environments, and this makes the problem much harder. In this paper, we present a robust optimization framework for maximizing the aggregate QoS in the presence of random failures. We introduce the notion of K-failure to bound the effect of random failures on schedulability. Using this notion we define the concept of K-robustness that quantifies the degree of robustness on QoS guarantee in a probabilistic sense. The parameter K helps to tradeoff achievable QoS versus robustness. The proposed robust framework produces optimal solutions through distributed computations on the basis of Lagrangian duality, and we present some implementation techniques. Our simulation results show that the proposed framework can probabilistically guarantee sub-optimal QoS which remains feasible even in the presence of random failures.
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The foot and the ankle are small structures commonly affected by disorders, and their complex anatomy represent significant diagnostic challenges. SPECT/CT Image fusion can provide missing anatomical and bone structure information to functional imaging, which is particularly useful to increase diagnosis certainty of bone pathology. However, due to SPECT acquisition duration, patient’s involuntary movements may lead to misalignment between SPECT and CT images. Patient motion can be reduced using a dedicated patient support. We aimed at designing an ankle and foot immobilizing device and measuring its efficacy at improving image fusion. Methods: We enrolled 20 patients undergoing distal lower-limb SPECT/CT of the ankle and the foot with and without a foot holder. The misalignment between SPECT and CT images was computed by manually measuring 14 fiducial markers chosen among anatomical landmarks also visible on bone scintigraphy. Analysis of variance was performed for statistical analysis. Results: The obtained absolute average difference without and with support was 5.1±5.2 mm (mean±SD) and 3.1±2.7 mm, respectively, which is significant (p<0.001). Conclusion: The introduction of the foot holder significantly decreases misalignment between SPECT and CT images, which may have clinical influence in the precise localization of foot and ankle pathology.
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Penalty and Barrier methods are normally used to solve Nonlinear Optimization Problems constrained problems. The problems appear in areas such as engineering and are often characterised by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot net used. A Java based API was implemented, including only derivative-free optimizationmethods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalisation when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the prosed penalty function besides being very robust also exhibits a very good performance.