optimal algorithm

Recently there has been a great deal of interest in performance evalution of parallel simulation. Most work is devoted to the time complexity and assumes that the amount of memory available for parallel simulation is unlimited. This paper studies the space complexity of parallel simulation. Our goal is to design an efficient memory management protocol which guarantees that the memory consumption of parallel simulation is of the same order as sequential simulation. (Such an algorithm is referred to as a optimal .) First, we derive the relationships among the space complexities of sequential simulation, Chandy-Misra simulation [2], and Time Warp simulation [7]. We show that Chandy-Misra may consume more storage than sequential simulation, or vice versa. Then we show that Time Warp never consumes less memory than sequential simulation. Then we describe cancelback , an optimal Time Warp memory management protocol proposed by Jefferson. Although cancelback is considered to be complete so...

The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial

The long-term hydrothermal scheduling is one of the most important problems to be solved in the power systems area. This problem aims to obtain an optimal policy, under water (energy) resources uncertainty, for hydro and thermal plants over a multi-annual planning horizon. It is natural to model the problem as a multi-stage stochastic program, a class of models for which algorithms have been developed. The original stochastic process is represented by a finite scenario tree and, because of the large number of stages, a sampling-based method such as the Stochastic Dual Dynamic Programming (SDDP) algorithm is required. The purpose of this paper is two-fold. Firstly, we study the application of two alternative sampling strategies to the standard Monte Carlo—namely, Latin hypercube sampling and randomized quasi-Monte Carlo—for the generation of scenario trees, as well as for the sampling of scenarios that is part of the SDDP algorithm. Secondly, we discuss the formulation of stopping criteria for the optimization algorithm in terms of statistical hypothesis tests, which allows us to propose an alternative criterion that is more robust than that originally proposed for the SDDP. We test these ideas on a problem associated with the whole Brazilian power system, with a three-year planning horizon.

Lexicographic construction is a greedy algorithm that produces error correcting codes known as lexicodes. The most surprising fact about lexicodes is that they possess some distinctive characteristics, contrary to the first impression that they do not have any interesting structure. In this project we observe these distinctive characteristics of the lexicodes in order to produce the optimal algorithm for the

Groundwater long-term monitoring (LTM) is required to assess the performance of groundwater remediation and human being health risk at post-closure sites where groundwater contaminants are still present. The large number of sampling locations, number of constituents to be monitored, and the frequency of the sampling make the LTM costly, especially since LTM may be required over several decades. An optimization

This paper presents the evaluation of the performance, in terms of uncertainty, of a tool designed to estimate the main parameters of a model of a photovoltaic panel (PVP) under real and/or simulated working conditions. The presented tool permits the characterization of the panel, and it is useful to predict its behavior in whatever working condition; in this way, it is possible to compare the actual and expected performance to prevent any decrease in the output power, so permitting the replacement of the monitored module before it goes out of order or its efficiency falls under a given threshold. The well-known two-diode model is used to estimate the parameters of the electrical equivalent circuit of the PVP and to simulate the I -V and P-V characteristic curves in any given environmental condition of irradiance and/or temperature. The model and the estimation algorithm are implemented with MATLAB functions, whereas data acquisition and result presentation are managed by a LabVIEW graphics user interface. The presented tool has been validated against an experimentally characterized PVP. The environmental parameters of the model such as irradiance and temperature have been set (with their respective uncertainties) during simulations or directly measured during the outdoor tests, whereas the others parameters have been evaluated using a best-fit algorithm on the measured data. The estimation is based on the minimization of a new objective function and on a modified expression of the model resistances, which differ from those mentioned in the available literature. After a review of the state of the art, this paper provides the description of the estimation technique and its validation by means of simulations and experiments. Some results are also provided to illustrate the performance of the proposed test method.

Abstract In this paper, an investigation of the behavior of a recently defined hybrid algorithm for continuous variables electromagnetic optimization problems is presented. This algorithm makes use of the nonlinear simplex method as a principal optimizator, and a greedy genetic algorithm to explore the search space. The algorithm is greatly affected by the tuning of some parameters. Referring to a solenoids system optimal design problem, a detailed comparison of performances obtained implementing a metaheuristic tuning is presented

The choice of an appropriate social rate of discount is critical in the decision-making process on public investments. In this paper we review the literature on social discounting, and address in particular a recently growing field of related research, that is, individual time preferences. We argue that an explicit consideration and analysis of the behaviour of individuals regarding the concept and the use of an appropriate social discount rate are essential for balanced decision making in the public sector, especially, though not exclusively, in the field of resource or environmental policy.

3) Calculate a new control input that minimizes the cost function, 4) Repeat steps 2 and 3 until desired minimization is achieved, 5) Send the first control input, to the plant, 6) Repeat entire process for each time step. The computational performance of a GPC implementation is ...

The detection and localisation of partial discharge (PD) activities is gaining importance in maintaining operational transformers and diagnosing those which are faulty. Ultra-high-frequency (UHF) techniques have been applied successfully to detection of PD activities in HV transformers because of their sensitivity to fast transient pulses. When an UHF PD monitoring system is applied, one of the crucial pieces of information

The memory dumping problem arises in the context of planning and scheduling activities of the Mars Express mission of the European Space Agency. The problem consists of scheduling scientific data transmission from Mars to the Earth. A previously developed algorithm computes robust schedules in a heuristic way and iter-atively improves the schedule robustness by solving a sequence of max-flow problems. We present a linear programming algorithm to compute schedules of max-imum robustness, providing provably optimal solutions in a very short computing time. We also give neces-sary and sufficient conditions to characterize “easy ” and “difficult ” instances, such that the former ones can be solved directly without any optimization algorithm.

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the

This paper presents a method for the design of
shell-type, single-phase distribution transformers to obtain
the manufacturing specifications. The method is simple, efficient
and accurate. By an exhaustive analysis, it is concluded
that the obtained solution is the global optimum. The following
constraints are imposed: excitation current, no-load
losses, total losses, impedance and efficiency. The methodology
of this paper requires only six input data: transformer
rating, low voltage, high voltage, connection of low-voltage
coil, connection of high-voltage coil, and frequency. These
data are included in the transformer nameplate. In this paper,
the minimization of the following four objective functions
is considered: total owing cost, mass, total losses and material
cost. The consideration of these four objective functions
is implemented automatically by running the optimization
algorithm four times without intervention of a designer. Consequently, transformer manufacturers save design man-hours
and increase capacity. A design example on a 25 kVA transformer
is presented for illustration. The optimized solutions of transformer design are validated with laboratory and process
measurements.

A heuristic-based optimization algorithm is proposed in this paper for on-line scheduling and assignment of preventive maintenance jobs to processors, to minimize under availability constraints, on a given time-window, the total cost of the maintenance operations of a distributed system. This algorithm minimizes the cost of discharge of preventive maintenance tasks or jobs, while assigning the tasks along with balancing the processors load. It is shown that the problem is NP-hard. To solve it, the concept of job emergency is introduced and the priority rule for total flow time (PRTF) criterion is used in an adapted heuristic job-scheduling model. In addition, the algorithm considers the constraints of precedence among consecutive standby jobs and their emergency. It is depicted the specific properties of the proposed heuristic allowing jobs scheduling in the right order. Computational results illustrate the efficiency of the approach implemented on different system configurations.