Today Optimization Research Articles

Optimization of the Underframe of the Sultan Wind Turbine V5 Using the Optimization Topology Method

Sultan wind turbine is one of the products of the New Renewable Energy Engineering Lab, Faculty of Engineering Untirta which has developed from year to year until now. To keep up with industrial developments in today's era, renewal is needed to improve and update the technology contained in the sultan wind turbine. In particular, today's optimization topologies are seen as providing the possibility to realize truly manufacturing-optimized designs through topology optimization. A topological optimization is an approach that is considered powerful in design because it contributes to designs that can save energy, materials ,and time that cannot be achieved economically using other manufacturing processes. A topological optimization, as it is often called, computer configuration of the best material over 3D space, usually represented as a grid, to satisfy or optimize physical parameters. Designers using these automated systems often seek to understand the interaction of physical constraints with the final design and their implications for other physical characteristics. Such understanding is a challenge to using a visualization approach to explore the design solution space. The essence of our new approach is to summarize an ensemble of solutions by automatically selecting a set of examples and parameterizing a design space.

A novel fuzzy based weighted aggregation based multi objective function for AVR optimization

Today optimization algorithms are widely used in every application to increase quality, quantity and efficiency of making products as well as to minimize the production cost. Most of the techniques applied on different applications try to satisfy more than one parameter of interest in the design problem. In doing so, an objective function based on weighted aggregation has been designed to fulfill multi-objective optimization (MOO). A lot of computational time and energy is wasted in tuning the value of weighting factor in terms of number of trials each having hundreds of iterations to achieve the optimum solution. To reduce such tedious practice of adjustment of weighting factor with multiple iterations, Fuzzy technique is proposed for auto-tuning of weighting factor in this paper that will benefit the researchers who are working upon optimization of their designed objectives using artificial intelligence techniques. This paper proposes MOO settlement method that does not require complex mathematical equations in order to simplify the weight finding problem of weighted aggregation objective function (WAOF). The results have been compared in terms of time and space efficiency to show the importance of Fuzzy-WAOF (F-WAOF). Further the results taken on Automatic Voltage Regulator (AVR) system for set point tracking, load disturbance, controller effort and modelling errors, prove the superior performance of the proposed method as compared to state of the art techniques.

Goal Programming Approach for Carrying People with Physical Disabilities

Most of today's optimization efforts aim to reduce costs, time or the number of resources used. However, optimization efforts should consider other factors as important as these, such as facilitating the lives of the disabled, elderly and pregnant and helping them in their daily lives. In this study, the Nuh Naci Yazgan (NNY) University (Kayseri/Turkey) personnel transport problems were discussed. The NNY University provides a shuttle service to bring employees to school at the start of the work and to leave them at home after work. In order to shorten the collection / distribution time and the total distance travelled, the service vehicle does not leave / pick up all employees in front of their homes. Instead, the employees are picked up / dropped at appropriate locations on an intuitively determined route. Since only the time and cost savings are taken into account when determining the service route, some employees have a long walking distance to the service route. This creates a very important problem, especially for the disabled and pregnant workers. In this study, a new mathematical model is proposed which takes into consideration the physical disadvantages and occupational positions of the employees in order to determine the shortest vehicle route. The results show that the proposed model can significantly reduce walking distances of physically disabled people without compromising the total distance travelled by the vehicle.

Selectivity estimation for range predicates using lightweight models

Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today's optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. While their straightforward application does not outperform even simple baselines, we propose two simple yet effective design choices, i.e., regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.

Review of numerical optimization techniques for meta-device design [Invited

Optimization techniques have been indispensable for designing high-performance meta-devices targeted to a wide range of applications. In fact, today optimization is no longer an afterthought and is a fundamental tool for many optical and RF designers. Still, many devices presented in recent literature do not take advantage of optimization techniques. This paper seeks to address this by presenting both an introduction to and a review of several of the most popular techniques currently used for meta-device design. Additionally, emerging techniques like topology optimization and multi-objective optimization and their context to device design are thoroughly discussed. Moreover, attention is given to future directions in meta-device optimization such as surrogate-modeling and deep learning which have the potential to disrupt the fields of optical and radio frequency (RF) inverse-design. Finally, many design examples from the literature are presented and a flow-chart that provides guidance on how best to apply these optimization algorithms to a given problem is provided for the reader.

Lightweight graphical models for selectivity estimation without independence assumptions

As a result of decades of research and industrial development, modern query optimizers are complex software artifacts. However, the quality of the query plan chosen by an optimizer is largely determined by the quality of the underlying statistical summaries. Small selectivity estimation errors, propagated exponentially, can lead to severely sub-optimal plans. Modern optimizers typically maintain one-dimensional statistical summaries and make the attribute value independence and join uniformity assumptions for efficiently estimating selectivities. Therefore, selectivity estimation errors in today's optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all the attributes in the database into small, usually two-dimensional distributions. We describe several optimizations that can make selectivity estimation highly efficient, and we present a complete implementation inside PostgreSQL's query optimizer. Experimental results indicate an order of magnitude better selectivity estimates, while keeping optimization time in the range of tens of milliseconds.

Exact cardinality query optimization for optimizer testing

The accuracy of cardinality estimates is crucial for obtaining a good query execution plan. Today's optimizers make several simplifying assumptions during cardinality estimation that can lead to large errors and hence poor plans. In a scenario such as query optimizer testing it is very desirable to obtain the "best" plan, i.e., the plan produced when the cardinality of each relevant expression is exact. Such a plan serves as a baseline against which plans produced by using the existing cardinality estimation module in the query optimizer can be compared. However, obtaining all exact cardinalities by executing appropriate subexpressions can be prohibitively expensive. In this paper, we present a set of techniques that makes exact cardinality query optimization a viable option for a significantly larger set of queries than previously possible. We have implemented this functionality in Microsoft SQL Server and we present results using the TPC-H benchmark queries that demonstrate their effectiveness.

Entropy production and exergy destruction: Part II—illustrative technologies

In a previous companion article, we pointed out that inter-technology synergies will become a generic feature of coming hydrogen age systems. These synergies will develop between sources, currencies and technologies. Unfortunately, today's optimization methodologies, based on energy rather than exergy optics, are often a misleading basis for optimization. To illustrate how the energy and exergy optics give quite startlingly different perceptions, this article uses both approaches to examine a range of technologies from civilization's energy systems, including hydroelectric generating stations at Niagara Falls, a coal-fired electricity generating station and home heating systems. Some observations are provided on how the results apply to fuel cells, which can be used for electricity generation or to cogenerate electricity and heat. The main objectives of this work are to understand better the nature of thermodynamic efficiencies and losses, to see clearly where losses should be attributed, and to more accurately identify how to improve efficiencies.

Supply chain optimization of continuous process industries with sustainability considerations

Today's optimization of continuous process industries should be exerted on the scope of whole enterprise supply chain, from the raw material purchase stage to product distribution stage, with sustainability considerations. Sustainability involves the mutliple objectives of social, economic, resources and environmental sustainability; some of them are conflicting. In this study, goal programming (GP) model is proposed to address this multi-objective problem with the integration of non-relaxable constraints and relaxable constraints. The analytic hierarchy process (AHP), a multi-objective decision making method, is used to evaluate the priorities of goals and weights of deviation variables. The application of this approach is illustrated through a case study on sustainable supply chain optimization and scheduling of a petrochemical complex. The results obtained show that this approach is a viable tool and offers good communication with decision-maker.