27 research outputs found
Minimizing Energy Consumption and Carbon Emissions of Aging Buildings
AbstractThe building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model
Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection
This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and furthermore it aids in interpreting the model. A clinical dataset, based on heart failure, is used to illustrate the nature of the problem and to show the effectiveness of the techniques developed. Clinical datasets are sometimes characterised as having many variables. For instance, blood biochemistry data has more than 60 variables that have led to complexities in developing predictions of outcomes using machine-learning and other algorithms. Hence, techniques to make them moretractable are required. Genetic Algorithms can provide an efficient and low numerically complex method for effectively selecting features. In this paper, a way to estimate the number of required variables is presented, and a genetic algorithm is used in a âwrapperâ form to select features for a case study of heart failure data.Additionally, different initial populations and termination conditions are used to arrive at a set of optimal features, and these are then compared with the features obtained using traditional methodologies. The paper provides a framework for estimating the number of variables and generations required for a suitable solution
Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic
[EN] The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the studentsâ desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authorsâ best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper.SIMinisterio de Ciencia, InnovaciĂłn y Universidade
An iterative method based FLC-SLM system design for forming multiple complex structures simultaneously in 3D volume with tissue
Complex structure formation and fast focusing of light inside or through
turbid media is a challenging task due to refractive index heterogeneity,
random light scattering and speckle noise formation. Here, we have proposed a
weighted-mutation assisted genetic algorithm (WMA-GA) with an R-squared metric
based fitness function that advances the contrast, resolution, focuses light
tightly and does fast convergence for both simple and complex structure
formation through the scattering media. As a compatible system with the binary
WMA-GA, we have presented a fast, cost-effective, and robust iterative
wavefront shaping system design with an affordable ferroelectric liquid crystal
(FLC) based binary-phase spatial light modulator (SLM). The proposed wavefront
shaping system design has been used to construct multiple complex
hetero-structures simultaneously in 3D volume by an optimized single
phase-mask. The WMA-GA and the prototype system have been validated with 120,
220, 450, and 600 grit ground glass diffusers along with 323, 588, and 852
{\mu}m thick fresh chicken tissues including fluorescence in it. We have
demonstrated the robustness of the proposed method to control the photon-in and
photon-out from a localized fluorescent dye embedded in the tissue. The
detailed results show that the proposed class of algorithm-backed integrated
system converges fast with higher contrast and advances the resolution.Comment: 17 pages, 13 figure
Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning
Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes.
Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy
Lyapunov function search method for analysis of nonlinear systems stability using genetic algorithm
This paper considers a wide class of smooth continuous dynamic nonlinear
systems (control objects) with a measurable vector of state. The problem is to
find a special function (Lyapunov function), which in the framework of the
second Lyapunov method guarantees asymptotic stability for the above described
class of nonlinear systems. It is well known that the search for a Lyapunov
function is the "cornerstone" of mathematical stability theory. Methods for
selecting or finding the Lyapunov function to analyze the stability of closed
linear stationary systems, as well as for nonlinear objects with explicit
linear dynamic and nonlinear static parts, have been well studied (see works by
Lurie, Yakubovich, Popov, and many others). However, universal approaches to
the search for the Lyapunov function for a more general class of nonlinear
systems have not yet been identified. There is a large variety of methods for
finding the Lyapunov function for nonlinear systems, but they all operate
within the constraints imposed on the structure of the control object. In this
paper we propose another approach, which allows to give specialists in the
field of automatic control theory a new tool/mechanism of Lyapunov function
search for stability analysis of smooth continuous dynamic nonlinear systems
with measurable state vector. The essence of proposed approach consists in
representation of some function through sum of nonlinear terms, which are
elements of object's state vector, multiplied by unknown coefficients, raised
to positive degrees. Then the unknown coefficients are selected using genetic
algorithm, which should provide the function with all necessary conditions for
Lyapunov function (in the framework of the second Lyapunov method).Comment: in Russian languag
Optimization of Process Flowsheets through Metaheuristic Techniques
This book presents a multi-objective optimization framework for optimizing
chemical processes. The proposed framework implements a link between process
simulators and metaheuristic techniques. The proposed approach is general, and
there can be used any process simulator and any metaheuristic technique. This
book shows how to implement links between different process simulators such as
Aspen PlusÂź, HYSYSÂź, SuperPro DesignerÂź, and others, linked to metaheuristic techniques implemented in MatlabÂź, ExcelÂź, C++, or other programs. This
way, the proposed framework allows optimizing any process flowsheet implemented in the process simulator and using the metaheuristic technique, and this
way the numerical complications through the optimization process can be eliminated. Furthermore, the proposed framework allows using the thermodynamic,
design, and constitutive equations implemented in the process simulator to implement any process