Direct search optimization application programming interface with remote access

Search Optimization methods are needed to solve optimization problems where the objective function and/or constraints functions might be non differentiable, non convex or might not be possible to determine its analytical expressions either due to its complexity or its cost (monetary, computational, time,...). Many optimization problems in engineering and other fields have these characteristics, because functions values can result from experimental or simulation processes, can be modelled by functions with complex expressions or by noise functions and it is impossible or very difficult to calculate their derivatives. Direct Search Optimization methods only use function values and do not need any derivatives or approximations of them. In this work we present a Java API that including several methods and algorithms, that do not use derivatives, to solve constrained and unconstrained optimization problems. Traditional API access, by installing it on the developer and/or user computer, and remote API access to it, using Web Services, are also presented. Remote access to the API has the advantage of always allow the access to the latest version of the API. For users that simply want to have a tool to solve Nonlinear Optimization Problems and do not want to integrate these methods in applications, also two applications were developed. One is a standalone Java application and the other a Web-based application, both using the developed API.

RSS and LEA adaptation for indoor location using fingerprinting

Fingerprinting is an indoor location technique, based on wireless networks, where data stored during the offline phase is compared with data collected by the mobile device during the online phase. In most of the real-life scenarios, the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node and the ones stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.

Improvement of a filters method in a derivative free optimization

Constraints nonlinear optimization problems can be solved using penalty or barrier functions. This strategy, based on solving the problems without constraints obtained from the original problem, have shown to be e ective, particularly when used with direct search methods. An alternative to solve the previous problems is the lters method. The lters method introduced by Fletcher and Ley er in 2002, , has been widely used to solve problems of the type mentioned above. These methods use a strategy di erent from the barrier or penalty functions. The previous functions de ne a new one that combine the objective function and the constraints, while the lters method treat optimization problems as a bi-objective problems that minimize the objective function and a function that aggregates the constraints. Motivated by the work of Audet and Dennis in 2004, using lters method with derivative-free algorithms, the authors developed works where other direct search meth- ods were used, combining their potential with the lters method. More recently. In a new variant of these methods was presented, where it some alternative aggregation restrictions for the construction of lters were proposed. This paper presents a variant of the lters method, more robust than the previous ones, that has been implemented with a safeguard procedure where values of the function and constraints are interlinked and not treated completely independently.

Filters method in direct search optimization, new measures to admissibility

Constrained nonlinear optimization problems are usually solved using penalty or barrier methods combined with unconstrained optimization methods. Another alternative used to solve constrained nonlinear optimization problems is the lters method. Filters method, introduced by Fletcher and Ley er in 2002, have been widely used in several areas of constrained nonlinear optimization. These methods treat optimization problem as bi-objective attempts to minimize the objective function and a continuous function that aggregates the constraint violation functions. Audet and Dennis have presented the rst lters method for derivative-free nonlinear programming, based on pattern search methods. Motivated by this work we have de- veloped a new direct search method, based on simplex methods, for general constrained optimization, that combines the features of the simplex method and lters method. This work presents a new variant of these methods which combines the lters method with other direct search methods and are proposed some alternatives to aggregate the constraint violation functions.

A web-based tool to evaluate the iterative processes of direct search methods

Constrained and unconstrained Nonlinear Optimization Problems often appear in many engineering areas. In some of these cases it is not possible to use derivative based optimization methods because the objective function is not known or it is too complex or the objective function is non-smooth. In these cases derivative based methods cannot be used and Direct Search Methods might be the most suitable optimization methods. An Application Programming Interface (API) including some of these methods was implemented using Java Technology. This API can be accessed either by applications running in the same computer where it is installed or, it can be remotely accessed through a LAN or the Internet, using webservices. From the engineering point of view, the information needed from the API is the solution for the provided problem. On the other hand, from the optimization methods researchers’ point of view, not only the solution for the problem is needed. Also additional information about the iterative process is useful, such as: the number of iterations; the value of the solution at each iteration; the stopping criteria, etc. In this paper are presented the features added to the API to allow users to access to the iterative process data.

Direct-search penalty/barrier methods

In Nonlinear Optimization Penalty and Barrier Methods are normally used to solve Constrained Problems. There are several Penalty/Barrier Methods and they are used in several areas from Engineering to Economy, through Biology, Chemistry, Physics among others. In these areas it often appears Optimization Problems in which the involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. In this work some Penalty/Barrier functions are tested and compared, using in the internal process, Derivative-free, namely Direct Search, methods. This work is a part of a bigger project involving the development of an Application Programming Interface, that implements several Optimization Methods, to be used in applications that need to solve constrained and/or unconstrained Nonlinear Optimization Problems. Besides the use of it in applied mathematics research it is also to be used in engineering software packages.

Resistance of Aedes aegypti to temephos and adaptive disadvantages

OBJECTIVE To evaluate the resistance of Aedes aegypti to temephos Fersol 1G (temephos 1% w/w) associated with the adaptive disadvantage of insect populations in the absence of selection pressure. METHODS A diagnostic dose of 0.28 mg a.i./L and doses between 0.28 mg a.i./L and 1.40 mg a.i./L were used. Vector populations collected between 2007 and 2008 in the city of Campina Grande, state of Paraíba, were evaluated. To evaluate competition in the absence of selection pressure, insect populations with initial frequencies of 20.0%, 40.0%, 60.0%, and 80.0% resistant individuals were produced and subjected to the diagnostic dose for two months. Evaluation of the development of aquatic and adult stages allowed comparison of the life cycles in susceptible and resistant populations and construction of fertility life tables. RESULTS No mortality was observed in Ae. aegypti populations subjected to the diagnostic dose of 0.28 mg a.i./L. The decreased mortality observed in populations containing 20.0%, 40.0%, 60.0%, and 80.0% resistant insects indicates that temephos resistance is unstable in the absence of selection pressure. A comparison of the life cycles indicated differences in the duration and viability of the larval phase, but no differences were observed in embryo development, sex ratio, adult longevity, and number of eggs per female. CONCLUSIONS The fertility life table results indicated that some populations had reproductive disadvantages compared with the susceptible population in the absence of selection pressure, indicating the presence of a fitness cost in populations resistant to temephos.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Experimental measuring procedure for the friction torque in rolling bearings

The reduction of the power loss generated in mechanical transmissions and the use of low friction biodegradable lubricants has been attracting considerable attention in recent times. Therefore, it is necessary to develop methods to test and evaluate the performance of such lubricants and compare them with conventional ones. In this sense, a Four-Ball Machine was modified allowing the test of rolling bearings. A 51107 thrust ball bearing was used to test two different greases and the corresponding base oils. Friction torque and operating temperatures were continuously monitored to quantify the power loss and the heat evacuation for each lubricant tested. Copyright © 2010 John Wiley & Sons, Ltd.

Vocal warm-up and breathing training for teachers: randomized clinical trial

OBJECTIVE To compare the effectiveness of two speech therapy interventions, vocal warm-up and breathing training, focusing on teachers’ voice quality.METHODS A single-blind, randomized, parallel clinical trial was conducted. The research included 31 20 to 60-year old teachers from a public school in Salvador, BA, Northeasatern Brazil, with minimum workloads of 20 hours a week, who have or have not reported having vocal alterations. The exclusion criteria were the following: being a smoker, excessive alcohol consumption, receiving additional speech therapy assistance while taking part in the study, being affected by upper respiratory tract infections, professional use of the voice in another activity, neurological disorders, and history of cardiopulmonary pathologies. The subjects were distributed through simple randomization in groups vocal warm-up (n = 14) and breathing training (n = 17). The teachers’ voice quality was subjectively evaluated through the Voice Handicap Index (Índice de Desvantagem Vocal, in the Brazilian version) and computerized voice analysis (average fundamental frequency, jitter, shimmer, noise, and glottal-to-noise excitation ratio) by speech therapists.RESULTS Before the interventions, the groups were similar regarding sociodemographic characteristics, teaching activities, and vocal quality. The variations before and after the intervention in self-assessment and acoustic voice indicators have not significantly differed between the groups. In the comparison between groups before and after the six-week interventions, significant reductions in the Voice Handicap Index of subjects in both groups were observed, as wells as reduced average fundamental frequencies in the vocal warm-up group and increased shimmer in the breathing training group. Subjects from the vocal warm-up group reported speaking more easily and having their voices more improved in a general way as compared to the breathing training group.CONCLUSIONS Both interventions were similar regarding their effects on the teachers’ voice quality. However, each contribution has individually contributed to improve the teachers’ voice quality, especially the vocal warm-up.TRIAL RECORD NCT02102399, “Vocal Warm-up and Respiratory Muscle Training in Teachers”.

Adaptive tackling of the swinging problem for a 2 DOF crane – payload system

The control of a crane carrying its payload by an elastic string corresponds to a task in which precise, indirect control of a subsystem dynamically coupled to a directly controllable subsystem is needed. This task is interesting since the coupled degree of freedom has little damping and it is apt to keep swinging accordingly. The traditional approaches apply the input shaping technology to assist the human operator responsible for the manipulation task. In the present paper a novel adaptive approach applying fixed point transformations based iterations having local basin of attraction is proposed to simultaneously tackle the problems originating from the imprecise dynamic model available for the system to be controlled and the swinging problem, too. The most important phenomenological properties of this approach are also discussed. The control considers the 4th time-derivative of the trajectory of the payload. The operation of the proposed control is illustrated via simulation results.

Fixed point transformations in the adaptive control of fractional-order MIMO systems

Though the formal mathematical idea of introducing noninteger order derivatives can be traced from the 17th century in a letter by L’Hospital in which he asked Leibniz what the meaning of D n y if n = 1/2 would be in 1695 [1], it was better outlined only in the 19th century [2, 3, 4]. Due to the lack of clear physical interpretation their first applications in physics appeared only later, in the 20th century, in connection with visco-elastic phenomena [5, 6]. The topic later obtained quite general attention [7, 8, 9], and also found new applications in material science [10], analysis of earth-quake signals [11], control of robots [12], and in the description of diffusion [13], etc.

Development of a meso-microscale coupling procedure for site assessment in complex terrain

A procedure for coupling mesoscale and CFD codes is presented, enabling the inclusion of realistic stratification flow regimes and boundary conditions in CFD simulations of relevance to site and resource assessment studies in complex terrain. Two distinct techniques are derived: (i) in the first one, boundary conditions are extracted from mesoscale results to produce time-varying CFD solutions; (ii) in the second case, a statistical treatment of mesoscale data leads to steady-state flow boundary conditions believed to be more representative than the idealised profiles which are current industry practice. Results are compared with measured data and traditional CFD approaches.

Effects of acupressure on progress of labor and cesarean section rate: randomized clinical trial

OBJECTIVE To analyze the effects of acupressure at the SP6 point on labor duration and cesarean section rates in parturients served in a public maternity hospital.METHODS This controlled, randomized, double-blind, pragmatic clinical trial involved 156 participants with gestational age ≥ 37 weeks, cervical dilation ≥ 4 cm, and ≥ 2 contractions in 10 min. The women were randomly divided into an acupressure, placebo, or control group at a university hospital in an inland city in the state of Sao Paulo, Brazil, in 2013. Acupressure was applied to the SP6 point during contractions for 20 min.RESULTS The average labor duration was significantly different between the SP6 acupressure group [221.5 min (SD = 162.4)] versus placebo [397.9 min (SD = 265.6)] and versus control [381.9 min (SD = 358.3)] (p = 0.0047); however, the groups were similar regarding the cesarean section rates (p = 0.2526) and Apgar scores in the first minute (p = 0.9542) and the fifth minute (p = 0.7218) of life of the neonate.CONCLUSIONS The SP6 acupressure point proved to be a complementary measure to induce labor and may shorten the labor duration without causing adverse effects to the mother or the newborn. However, it did not affect the cesarean section rate.

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.