scholarly journals SENSITIVITY, RESOLUTION AND AMBIGUITY OF THE CRS STACK OPERATOR

2013 ◽  
Vol 31 (4) ◽  
pp. 643 ◽  
Author(s):  
Lourenildo W.B. Leite ◽  
Wildney W.S. Vieira
Keyword(s):  
Optimization Problem ◽  
System Analysis ◽  
Random Search ◽  
Synthetic Data ◽  
Optimization Methods ◽  
Statistical Properties ◽  
Least Square ◽  
Linear Behavior ◽  
Starting Point ◽  
Common Reflection Surface

ABSTRACT. This paper describes an investigation about the sensitivity and ambiguity of the Common Reflection Surface (CRS) stack operator parameters (v0, RNIP,RN, α0), and their resolution in terms of statistical properties of the solution of a nonlinear multi-parametric optimization problem for surface fitting between the forward model and a synthetic data, in the least-square sense. The sensitivity method is borrowed from dynamic system analysis and synthesis, and the definitions are based on the Miller-Murray model. The results are analyzed in terms of the CRS attributes search strategies during the stack process. The investigation principle is to combine global and local optimization methods to reach a minimum of the object function of minimization, where the problem matrix has a better linear relation to the parameters. A first search for a minimum is performed with a controlled random search method, followed by a gradient method for the last steps fo the optimization to calculate the data and parameter resolution and covariance matrices, and any further model statistical properties. The sensitivity functions are represented by the columns of the optimization problem matrix, and they in general exhibit a linear behavior instead of a convex form; as a result, this linear behavior establish thenecessity of a good starting point for the optimized multi-parametric attributes search.Keywords: CRS attributes, sensitivity analysis, resolution, ambiguity. RESUMO. Este trabalho descreve uma investigação sobre a sensitividade e ambiguidade dos parâmetros (v0,RNIP, RN, α0) do operador de empilhamento CRS, e suas resoluções em termos das propriedades estatísticas da solução de um problema não-linear multi-paramétrico do ajuste da superfície de um modelo direto à de dados sintéticos, no sentido do quadrados-mínimos. O método da sensitividade é adaptado da análise e síntese de sistemas dinâmicos, e as definições são baseadas no modelo Miller-Murray. Os resultados são analisados em termos das estratégias de busca dos atributos CRS durante o processo de empilhamento. O princípio da investigação é combinar métodos de otimização global e local para alcançar um mínimo da função objeto de minimização, onde a matriz do problema tem uma melhor relação linear com os parâmetros. A primeira busca de um mínimo é realizada com um método de busca aleatória controlada, seguida por um método do gradiente para os últimos passos da otimização para calcular as matrizes resolução e covariância dos dados e parâmetros, e quaisquer propiedades estatísticas do modelo. As funções sensitividade são representadas pelas colunas da matriz otimização do problema, e em geral elas exibem um comportamento linear em vez de uma forma convexa; como resultado, este comportamento linear estabelece a necessidade de um bom ponto inicial para a busca multi-paramétrica otimizada dos atributos.Palavras-chave: atributos CRS, análise de sensibilidade, resolução, ambiguidade.

Optimization Methods for Spectral Synthesizing of a Tuneable Colour Light Source

2018 ◽  
pp. 99-108
Author(s):  
Nina Carli ◽  
Armin Sperling ◽  
Grega Bizjak
Keyword(s):  
Light Source ◽  
Optimization Problem ◽  
Least Square Method ◽  
Optimization Methods ◽  
Least Square ◽  
Light Sources ◽  
Output Spectrum ◽  
Constrained Problem ◽  
Target Spectrum ◽  
Spectral Distributions

A method for a tuneable colour light source (TCLS) output spectrum synthesizing is described. A TCLS is a multichannel LED light source, which is able to mimic and produce different spectral distributions and can be used for the realization of different spectra, e.g. the spectra of different CIE standard illuminants. The synthesizing of output spectrum is actually an optimization problem of tuning the output spectrum of a TCLS to the target spectrum. It is also a so called constrained problem as output spectrum is produced by adding the weighted spectra of used light sources (e.g. singlecolour LEDs) and due to the fact that there is no “negative light”. Because of that usual optimization methods like least square method cannot be used. A novel synthesizing method based on a constrained optimization process was developed and tested on the laboratory TCLS to be used for calibration purposes. The developed synthesizing method, described in this paper, gives good results but comparison with more simple methods shows that also these can be successfully used.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency

2021 ◽  
Vol 11 (9) ◽  
pp. 3827
Author(s):  
Blazej Nycz ◽  
Lukasz Malinski ◽  
Roman Przylucki
Keyword(s):  
Electromagnetic Levitation ◽  
Optimization Methods ◽  
Calculation Model ◽  
Model Parameters ◽  
Starting Point ◽  
Low Efficiency ◽  
The Relationship ◽  
Reactive Metals ◽  
Electromagnetic Levitation Melting ◽  
Metal Melting

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

scholarly journals Example of Warehouse System Design Based on the Principle of Logistics

2021 ◽  
Vol 13 (8) ◽  
pp. 4492
Author(s):  
Janka Saderova ◽  
Andrea Rosova ◽  
Marian Sofranko ◽  
Peter Kacmary
Keyword(s):  
System Design ◽  
Design Process ◽  
System Analysis ◽  
Proper Function ◽  
Logistics Systems ◽  
Starting Point ◽  
Storage Area ◽  
Area Percentage ◽  
First In First Out ◽  
Percentage Ratio

The warehouse process, as one of many logistics processes, currently holds an irreplaceable position in logistics systems in companies and in the supply chain. The proper function of warehouse operations depends on, among other things, the type of the used technology and their utilization. The research in this article is focused on the design of a warehouse system. The selection of a suitable warehouse system is a current research topic as the warehouse system has an impact on warehouse capacity and utilization and on the speed of storage activities. The paper presents warehouse system design methodology that was designed applying the logistics principle-systematic (system) approach. The starting point for designing a warehouse system represents of the process of design logistics systems. The design process consists of several phases: project identification, design process paradigm selection, system analysis, synthesis, and project evaluation. This article’s contribution is the proposed methodology and design of the warehouse system for the specified conditions. The methodology was implemented for the design of a warehouse system in a cold box, which is a part of a distribution warehouse. The technology of pallet racking was chosen in the warehouse to store pallets. Pallets will be stored and removed by forklifts. For the specified conditions, the warehouse system was designed for two alternatives of racking assemblies, which are served by forklifts. Alternative 1—Standard pallet rack with wide aisles and Alternative 2—Pallet dynamic flow rack. The proposed systems were compared on the basis of selected indicators: Capacity—the number of pallet places in the system, Percentage ratio of storage area from the box area, Percentage ratio of handling aisles from the box area, Access to individual pallets by forklift, Investment costs for 1 pallet space in EUR. Based on the multicriteria evaluation, the Alternative 2 was chosen as the acceptable design of the warehouse system with storage capacity 720 pallet units. The system needs only two handling aisles. Loading and unloading processes are separate from each other, which means that there are no collisions with forklifts. The pallets with the goods are operated on the principle of FIFO (first in, first out), which will facilitate the control of the shelf life of batches or series of products. The methodology is a suitable tool for decision-making in selecting and designing a warehouse system.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

scholarly journals System Analysis of Decision Support in the Design of a Secondary School Network

2020 ◽  
Vol 26 (3) ◽  
pp. 363-370
Author(s):  
V. G. Mokrozub ◽  
◽  
S. A. Rachkova ◽  
F. I. Vshivkov ◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Secondary School ◽  
Secondary Schools ◽  
Optimization Problem ◽  
System Analysis ◽  
Functional Diagram ◽  
Information Models ◽  
School Network ◽  
The Russian Federation ◽  
Functional Blocks

The article describes the elements of the project of an automated decision support system in the design of a network of secondary schools: a functional diagram, a functional presentation, the problem of optimizing a network of secondary schools by the criterion of their total cost. The functional diagram is presented in IDEF0 format and contains a description of all information flows. The functional view defines the information models required to execute IDEF0 functional blocks. The optimization problem describes the initial data, solution results and restrictions set by regulatory documents for the implementation of educational activities in the Russian Federation.

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

scholarly journals Constructing Synthetic Samples

2016 ◽  
Vol 32 (1) ◽  
pp. 113-127
Author(s):  
Hua Dong ◽  
Glen Meeden
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Random Search ◽  
The United States ◽  
Simulation Studies ◽  
Synthetic Sample ◽  
Population Means ◽  
Adaptive Random Search ◽  
Random Search Algorithm ◽  
Two Populations

Abstract We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

Simulation und Optimierung kombiniert/A simulation-based framework for operational optimization of industrial energy systems

2020 ◽  
Vol 110 (01-02) ◽  
pp. 12-17
Author(s):  
Niklas Panten ◽  
Heiko Ranzau ◽  
Thomas Kohne ◽  
Daniel Moog ◽  
Eberhard Abele ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Cost Savings ◽  
Energy Systems ◽  
Decisive Role ◽  
Optimization Methods ◽  
Energy System ◽  
Significant Cost ◽  
Operational Optimization ◽  
Simulation Based ◽  
Industrial Energy

Die optimierte Betriebsweise von industriellen Energiesystemen ist eine Schlüsseltechnologie, um signifikante Kosteneinsparpotenziale durch Steigerung der Energieeffizienz und -flexibilität zu heben. Weil dabei eine Vielzahl dynamischer und stochastischer Einflüsse berücksichtigt werden müssen, spielt die Simulation des Energiesystems eine entscheidende Rolle. Zur Evaluierung unterschiedlicher Betriebsoptimierungsverfahren wird ein simulationsgestütztes Framework vorgestellt, welches bei KI (Künstliche Intelligenz)-Algorithmen unter anderem für das Anlernen mit synthetischen Daten verwendet werden kann.   The optimized operation of industrial energy systems is a key technology to unlock significant cost savings by increasing energy efficiency and flexibility. Since a variety of dynamic and stochastic influences must be considered, the simulation of the energy system plays a decisive role. A simulation-based framework is presented for evaluating various operational optimization methods, which can also be used for learning based on synthetic data with AI (artificial intelligence) algorithms.

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