Relevant bibliographies by topics / Parameter setting optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Parameter setting optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Parameter setting optimization"

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Xu, Xiu Fen. "Design of Machine Tool Setting and Parameter Optimization." Advanced Materials Research 706-708 (June 2013): 1132–35. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1132.

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To solve the existing problems in the NC machining process, the optimization design of cutting parameters in NC milling machine with genetic algorithm. With the maximum production efficiency as the optimization objective, the spindle speed, feed speed, milling width, depth and other parameters as optimal variables, establishes the optimization mathematical model of machine tool. The optimization results show that: parameters optimization can significantly improve the processing efficiency, and bring economic benefits for enterprises
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Jiang, Jiawei, Yanhong Wu, Hongyan Wang, Yakun Lv, Lei Qiu, and Daobin Yu. "Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization." Electronics 8, no. 2 (2019): 160. http://dx.doi.org/10.3390/electronics8020160.

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Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.
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LO, V. H. Y., G. Q. HUANG, K. C. CHENG, and Y. M. TSANG. "THE USE OF TAGUCHI METHODS IN POLISHING FOR QUALITY OPTIMIZATION." International Journal of Reliability, Quality and Safety Engineering 14, no. 03 (2007): 297–309. http://dx.doi.org/10.1142/s0218539307002660.

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Polishing process has not been thoroughly understood. The current practice of determining parameter setting for polishing process is done by trial and error approach and subjective experience of polish experts. This may not lead to optimal parameter settings. In this paper, Taguchi method is proposed to determine optimal parameter setting for rough polishing of stainless steel. Experiments are designed using orthogonal arrays. The optimal setting is obtained through average level analysis. Confirmation experiment showed that the roughness is satisfactory.
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Geem, Zong Woo. "Economic Dispatch Using Parameter-Setting-Free Harmony Search." Journal of Applied Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/427936.

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Economic dispatch is one of the popular energy system optimization problems. Recently, it has been solved by various phenomenon-mimicking metaheuristic algorithms such as genetic algorithm, tabu search, evolutionary programming, particle swarm optimization, harmony search, honey bee mating optimization, and firefly algorithm. However, those phenomenon-mimicking problems require a tedious and troublesome process of algorithm parameter value setting. Without a proper parameter setting, good results cannot be guaranteed. Thus, this study adopts a newly developed parameter-setting-free technique combined with the harmony search algorithm and applies it to the economic dispatch problem for the first time, obtaining good results. Hopefully more researchers in energy system fields will adopt this user-friendly technique in their own problems in the future.
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Kok, Kai Yit, and Parvathy Rajendran. "Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 1 (2020): 67–78. http://dx.doi.org/10.37936/ecti-cit.2020141.193991.

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This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.
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Corazza, Marco, Giovanni Fasano, Stefania Funari, and Riccardo Gusso. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization." Decisions in Economics and Finance 44, no. 1 (2021): 295–339. http://dx.doi.org/10.1007/s10203-021-00322-1.

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AbstractIn this paper, we amend a multi-criteria methodology known as MURAME, to evaluate the creditworthiness of a large sample of Italian Small and Medium-sized Enterprises, using as input their balance sheet data. This methodology produces results in terms of scoring and of classification into homogeneous rating classes. A distinctive goal of this paper is to consider a preference disaggregation method to endogenously determine some parameters of MURAME, by solving a nonsmooth constrained optimization problem. Because of the complexity of the involved mathematical programming problem, for its solution we use an evolutionary metaheuristic, coupled with a specific efficient initialization. This is combined with an unconstrained reformulation of the problem, which provides a reasonable compromise between the quality of the solution and the computational burden. An extensive numerical experience is reported, comparing an exogenous choice of MURAME parameters with our approach.
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Arunchai, Thongchai, Kawin Sonthipermpoon, Phisut Apichayakul, and Kreangsak Tamee. "Resistance Spot Welding Optimization Based on Artificial Neural Network." International Journal of Manufacturing Engineering 2014 (November 9, 2014): 1–6. http://dx.doi.org/10.1155/2014/154784.

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Resistance Spot Welding (RSW) is processed by using aluminum alloy used in the automotive industry. The difficulty of RSW parameter setting leads to inconsistent quality between welds. The important RSW parameters are the welding current, electrode force, and welding time. An additional RSW parameter, that is, the electrical resistance of the aluminum alloy, which varies depending on the thickness of the material, is considered to be a necessary parameter. The parameters applied to the RSW process, with aluminum alloy, are sensitive to exact measurement. Parameter prediction by the use of an artificial neural network (ANN) as a tool in finding the parameter optimization was investigated. The ANN was designed and tested for predictive weld quality by using the input and output data in parameters and tensile shear strength of the aluminum alloy, respectively. The results of the tensile shear strength testing and the estimated parameter optimization are applied to the RSW process. The achieved results of the tensile shear strength output were mean squared error (MSE) and accuracy equal to 0.054 and 95%, respectively. This indicates that that the application of the ANN in welding machine control is highly successful in setting the welding parameters.
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Schneidewind, Jörn, Mike Sips, and Daniel A. Keim. "An Automated Approach for the Optimization of Pixel-Based Visualizations." Information Visualization 6, no. 1 (2007): 75–88. http://dx.doi.org/10.1057/palgrave.ivs.9500150.

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During the last two decades, a wide variety of advanced methods for the visual exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which users or analysts have to select the right parameter settings from among many in order to construct insightful visualizations. The right choice of input parameters is essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. But finding the right parameters is often a tedious process and it becomes almost impossible for an analyst to find an optimal parameter setting manually because of the volume and complexity of today's data sets. Therefore, we propose a novel approach for automatically determining meaningful parameter- and attribute settings based on the combined analysis of the data space and the resulting visualizations with respect to a given task. Our technique automatically analyzes pixel images resulting from visualizations created from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real-world applications are provided to show the benefit of the proposed approach.
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Kumar, Anshuman, Chandramani Upadhyay, and Shashikant Shashikant. "Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA." World Journal of Engineering 18, no. 5 (2021): 720–33. http://dx.doi.org/10.1108/wje-12-2020-0643.

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Purpose In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA). Design/methodology/approach Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing. Findings It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand. Originality/value The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.
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Luo, Yi, Ke Zhang, Yi Chai, and Yingzhi Xiong. "Multi-Parameter-Setting Based on Data Original Distribution for DENCLUE Optimization." IEEE Access 6 (2018): 16704–11. http://dx.doi.org/10.1109/access.2018.2791203.

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Dissertations / Theses on the topic "Parameter setting optimization"

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Hepdogan, Seyhun. "META-RAPS: PARAMETER SETTING AND NEW APPLICATIONS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3493.

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ABSTRACT Recently meta-heuristics have become a popular solution methodology, in terms of both research and application, for solving combinatorial optimization problems. Meta-heuristic methods guide simple heuristics or priority rules designed to solve a particular problem. Meta-heuristics enhance these simple heuristics by using a higher level strategy. The advantage of using meta-heuristics over conventional optimization methods is meta-heuristics are able to find good (near optimal) solutions within a reasonable computation time. Investigating this line of research is justified because in most practical cases with medium to large scale problems, the use of meta-heuristics is necessary to be able to find a solution in a reasonable time. The specific meta-heuristic studied in this research is, Meta-RaPS; Meta-heuristic for Randomized Priority Search which is developed by DePuy and Whitehouse in 2001. Meta-RaPS is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element (Moraga, 2002). To date, Meta-RaPS had been applied to different types of combinatorial optimization problems and achieved comparable solution performance to other meta-heuristic techniques. The specific problem studied in this dissertation is parameter setting of Meta-RaPS. The topic of parameter setting for meta-heuristics has not been extensively studied in the literature. Although the parameter setting method devised in this dissertation is used primarily on Meta-RaPS, it is applicable to any meta-heuristic's parameter setting problem. This dissertation not only enhances the power of Meta-RaPS by parameter tuning but also it introduces a robust parameter selection technique with wide-spread utility for many meta-heuristics. Because the distribution of solution values generated by meta-heuristics for combinatorial optimization problems is not normal, the current parameter setting techniques which employ a parametric approach based on the assumption of normality may not be appropriate. The proposed method is Non-parametric Based Genetic Algorithms. Based on statistical tests, the Non-parametric Based Genetic Algorithms (NPGA) is able to enhance the solution quality of Meta-RaPS more than any other parameter setting procedures benchmarked in this research. NPGA sets the best parameter settings, of all the methods studied, for 38 of the 41 Early/Tardy Single Machine Scheduling with Common Due Date and Sequence-Dependent Setup Time (ETP) problems and 50 of the 54 0-1 Multidimensional Knapsack Problems (0-1 MKP). In addition to the parameter setting procedure discussed, this dissertation provides two Meta-RaPS combinatorial optimization problem applications, the 0-1 MKP, and the ETP. For the ETP problem, the Meta-RaPS application in this dissertation currently gives the best meta-heuristic solution performance so far in the literature for common ETP test sets. For the large ETP test set, Meta-RaPS provided better solution performance than Simulated Annealing (SA) for 55 of the 60 problems. For the small test set, in all four different small problem sets, the Meta-RaPS solution performance outperformed exiting algorithms in terms of average percent deviation from the optimal solution value. For the 0-1 MKP, the present Meta-RaPS application performs better than the earlier Meta-RaPS applications by other researchers on this problem. The Meta-RaPS 0-1 MKP application presented here has better solution quality than the existing Meta-RaPS application (Moraga, 2005) found in the literature. Meta-RaPS gives 0.75% average percent deviation, from the best known solutions, for the 270 0-1 MKP test problems.<br>Ph.D.<br>Department of Industrial Engineering and Management Systems<br>Engineering and Computer Science<br>Industrial Engineering and Management Systems
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Jankee, Christopher. "Optimisation par métaheuristique adaptative distribuée en environnement de calcul parallèle." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0480/document.

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Pour résoudre des problèmes d'optimisation discret de type boîte noire, de nombreux algorithmes stochastiques tels que les algorithmes évolutionnaires ou les métaheuristiques existent et se révèlent particulièrement efficaces selon le problème à résoudre. En fonction des propriétés observées du problème, choisir l'algorithme le plus pertinent est un problème difficile. Dans le cadre original des environnements de calcul parallèle et distribué, nous proposons et analysons différentes stratégies adaptative de sélection d'algorithme d'optimisation. Ces stratégies de sélection reposent sur des méthodes d'apprentissage automatique par renforcement, issu du domaine de l'intelligence artificielle, et sur un partage d'information entre les noeuds de calcul. Nous comparons et analysons les stratégies de sélection dans différentes situations. Deux types d'environnement de calcul distribué synchrone sont abordés : le modèle en île et le modèle maître-esclave. Sur l'ensemble des noeuds de manière synchrone à chaque itération la stratégie de sélection adaptative choisit un algorithme selon l'état de la recherche de la solution. Dans une première partie, deux problèmes OneMax et NK, l'un unimodal et l'autre multimodal, sont utilisés comme banc d'essai de ces travaux. Ensuite, pour mieux saisir et améliorer la conception des stratégies de sélection adaptatives, nous proposons une modélisation du problème d'optimisation et de son opérateur de recherche locale. Dans cette modélisation, une caractéristique importante est le gain moyen d'un opérateur en fonction de la fitness de la solution candidate. Le modèle est utilisé dans le cadre synchrone du modèle maître-esclave. Une stratégie de sélection se décompose en trois composantes principales : l'agrégation des récompenses échangées, la technique d'apprentissage et la répartition des algorithmes sur les noeuds de calcul. Dans une dernière partie, nous étudions trois scénarios et nous donnons des clés de compréhension sur l'utilisation pertinente des stratégies de sélection adaptative par rapport aux stratégies naïves. Dans le cadre du modèle maître-esclave, nous étudions les différentes façons d'agréger les récompenses sur le noeud maître, la répartition des algorithmes d'optimisation sur les noeuds de calcul et le temps de communication. Cette thèse se termine par des perspectives pour le domaine de l'optimisation stochastique adaptative distribuée<br>To solve discrete optimization problems of black box type, many stochastic algorithms such as evolutionary algorithms or metaheuristics exist and prove to be particularly effective according to the problem to be solved. Depending on the observed properties of the problem, choosing the most relevant algorithm is a difficult problem. In the original framework of parallel and distributed computing environments, we propose and analyze different adaptive optimization algorithm selection strategies. These selection strategies are based on reinforcement learning methods automatic, from the field of artificial intelligence, and on information sharing between computing nodes. We compare and analyze selection strategies in different situations. Two types of synchronous distributed computing environment are discussed : the island model and the master-slave model. On the set of nodes synchronously at each iteration, the adaptive selection strategy chooses an algorithm according to the state of the search for the solution. In the first part, two problems OneMax and NK, one unimodal and the other multimodal, are used as benchmarks for this work. Then, to better understand and improve the design of adaptive selection strategies, we propose a modeling of the optimization problem and its local search operator. In this modeling, an important characteristic is the average gain of an operator according to the fitness of the candidate solution. The model is used in the synchronous framework of the master-slave model. A selection strategy is broken down into three main components : the aggregation of the rewards exchanged, the learning scheme and the distribution of the algorithms on the computing nodes. In the final part, we study three scenarios, and we give keys to understanding the relevant use of adaptive selection strategies over naïve strategies. In the framework of the master-slave model, we study the different ways of aggregating the rewards on the master node, the distribution of the optimization algorithms of the nodes of computation and the time of communication. This thesis ends with perspectives in the field of distributed adaptive stochastic optimization
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Darvish, Arman. "User aid-based evolutionary computation for optimal parameter setting of image enhancement and segmentation." Thesis, 2011. http://hdl.handle.net/10155/209.

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Applications of imaging and image processing become a part of our daily life and find their crucial way in real-world areas. Accordingly, the corresponding techniques get more and more complicated. Many tasks are recognizable for a image processing chain, such as, filtering, color balancing, enhancement, segmentation, and post processing. Generally speaking, all of the image processing techniques need a control parameter setting. The better these parameters are set the better results can be achieved. Usually, these parameters are real numbers so search space is really large and brute-force searching is impossible or at least very time consuming. Therefore, the optimal setting of the parameters is an essential requirement to obtain desirable results. Obviously, we are faced with an optimization problem, which its complexity depends on the number of the parameters to be optimized and correlation among them. By reviewing the optimization methods, it can be understood that metaheuristic algorithms are the best candidates for these kind of problems. Metaheuristic algorithms are iterative approaches which can search very complex large spaces to come up with an optimal or close to optimal solution(s). They are able to solve black-box global optimization problems which are not solvable by classic mathematical methods. The first part of this thesis optimizes the control parameters for an eye-illusion, image enhancement, and image thresholding tasks by using an interactive evolutionary optimization approach. Eye illusion and image enhancement are subjective human perception-based issues, so, there is no proposed analytical fitness function for them. Their optimization is only possible through interactive methods. The second part is about setting of active contour (snake) parameters. The performance of active contours (snakes) is sensitive to its eight correlated control parameters which makes the parameter setting problem complex to solve. In this work, wehave tried to set the parameters to their optimal values by using a sample segmented image provided by an expert. As our case studies, we have used breast ultrasound, prostate ultrasound, and lung X-ray medical images. The proposed schemes are general enough to be investigated with other optimization methods and also image processing tasks. The achieved experimental results are promising for both directions, namely, interactive-based image processing and sample-based medical image segmentation.<br>UOIT
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Gibbs, Matthew S. "Real-coded genetic algorithm parameter setting for water distribution system optimisation." 2008. http://hdl.handle.net/2440/49644.

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The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, optimisation methods known as Genetic Algorithms (GAs) have been successfully adopted to assist in determining the best possible solutions to WDS optimisation problems for a number of years. Even though there has been extensive research demonstrating the potential of GAs for improving the design and operation of WDSs, the method has not been widely adopted in practice. There are a number of reasons that may contribute to this lack of uptake, including the following difficulties: (a) developing an appropriate fitness function that is a suitable description of the objective of the optimisation including all constraints, (b) making decisions that are required to select the most appropriate variant of the algorithm, (c) determining the most appropriate parameter settings for the algorithm, and (d) a reluctance of WDS operators to accept new methods and approaches. While these are all important considerations, the correct selection of GA parameter values is addressed in this thesis. Common parameters include population size, probability of crossover, and probability of mutation. Generally, the most suitable GA parameters must be found for each individual optimisation problem, and therefore it might be expected that the best parameter values would be related to the characteristics of the associated fitness function. The result from the work undertaken in this thesis is a complete GA calibration methodology, based on the characteristics of the optimisation problem. The only input required by the user is the time available before a solution is required, which is beneficial in the WDS operation optimisation application considered, as well as many others where computationally demanding model simulations are required. Two methodologies are proposed and evaluated in this thesis, one that considers the selection pressure based on the characteristics of the fitness function, and another that is derived from the time to convergence based on genetic drift, and therefore does not require any information about the fitness function characteristics. The proposed methodologies have been compared against other GA calibration methodologies that have been proposed, as well as typical parameter values to determine the most suitable method to determine the GA parameter values. A suite of test functions has been used for the comparison, including 20 complex mathematical optimisation problems with different characteristics, as well as realistic WDS applications. Two WDS applications have been considered: one that has previously been optimised in the literature, the Cherry Hills-Brushy Plains network; and a real case study located in Sydney, Australia. The optimisation problem for the latter case study is to minimise the pumping costs involved in operating the WDS, subject to constraints on the system, including minimum disinfectant concentrations. Of the GA calibration methods compared, the proposed calibration methodology that considered selection pressure determined the best solution to the problem, producing a 30% reduction in the electricity costs for the water utility operating the WDS. The comparison of the different calibration approaches demonstrates three main results: 1. that the proposed methodology produced the best results out of the different GA calibration methods compared; 2. that the proposed methodology can be applied in practice; and 3. that a correctly calibrated GA is very beneficial when solutions are required in a limited timeframe.<br>http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1325448<br>Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2008
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Visser, Jacobus. "A method of voltage tracking for power system applications." Diss., 2010. http://hdl.handle.net/2263/26685.

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An algorithm that is capable of estimating the parameters of non-stationary sinusoids in real-time lends application to various branches of engineering. Non-stationary sinusoids are sinusoidal signals with time-varying parameters. In this dissertation, a nonlinear filter is applied to power system applications to test its performance. The filter has a structure which renders it fully adaptive to tracking time variations in the parameters of the targeted sinusoid, including its phase and frequency. Mathematical properties of the differential equations which govern the proposed filter are presented. The performance of the proposed filter in the field of power systems is demonstrated with the aid of computer simulations and practical experimentations. The filter is applied to synchronous generator excitation control, voltage dip mitigation as well as the real-time estimation of symmetrical components. The parameter settings of the filter are tested and optimized for each of the applications. This dissertation demonstrates the simulation and experimental results of the filter when applied to the various power system applications. AFRIKAANS : 'n Filter wat bevoeglik is met die beraming van die parameters van beweeglike sinusoïdale in ware-tyd, kan bruikbaar aangewend word in verskeie takke van ingenieurswese. Beweeglike sinuskrommes is sinusoïdale seine met tyd-wisselende parameters. In hierdie verhandeling word `n nie-liniêre filter aangewend in verskeie kragstelseltoepassings om die werksverrigting van die filter te toets. Die filter het 'n struktuur wat dit toelaat om wisselende tydvariasies in die parameters van die teikensinusoïdaal op te spoor, insluitende die fase en frekwensie. Wiskundige eienskappe van die differensiaalvergelykings wat die voorgestelde filter beheer is ondersoek. Die werksverrigting van die voorgestelde filter in die veld van kragstelsels is gedemonstreer met die hulp van rekenaarsimulasies asook praktiese eksperimente. Die filter is toegepas tot opgewekte, sinkrone eksitasie-beheer, spanningsverlaging versagting, asook die ware tyd estimasie van simmetriese komponente. Die parameter verstellings van die filter is getoets en geoptimeer vir elk van die toepassings. Hierdie verhandeling demonstreer die simulering en eksperimentele resultate van die filter wat aangewend is vir verskeie kragstelseltoepassings. Copyright<br>Dissertation (MSc)--University of Pretoria, 2010.<br>Electrical, Electronic and Computer Engineering<br>unrestricted
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李翊僑. "Optimization of process parameters setting research -A case study of lognormal distribution." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/gtatjv.

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碩士<br>國立新竹教育大學<br>應用數學系碩士班<br>103<br>Optimum process parameters in the production process to set certain conditions to understand margin products, is one of the most important ring; each quality characteristics are an ideal target, in Chang et.al (2009) article, we discuss a condition characterized by the quality of "bigger is better", the optimal solution still exist on this paper to facilitate the multivariate normal distribution of the number of quality characteristics of the basic assumptions.
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Chen, Jen-Lian, and 陳建良. "Solving High-Dimension Optimization Problems by Using the Correct Setting of Particle Swarm Optimization Parameters, the Study." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/45351579580451516327.

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碩士<br>大葉大學<br>電機工程學系<br>100<br>Particle Swarm Optimization, PSO, proposed by Professor J. Kennedy and R. Eberhart in 1995, is one the current and attractive optimization algorithms studied all over the whole world nowadays. PSO is a new branch of soft computing as well. Its advantages are less parameter settings required and fast convergence of the algorithm with effective computational time. As we have known from the previous study of other researchers that appending mutation mechanism into PSO can prevent the PSO algorithm stagnated from the local trap, the dimension can be increased further for some solution findings of functions, such as, Sphere, Rastrigin and Rosenbrock. When the dimension is increased to 200, the efficiency of present PSO is still poor. For this reason, we have proposed further in this thesis that adding another inertia weight ω_p to the position equation, adding one-variable mutation mechanism to improve the chance of jumping stuck solution out of trap. For example, we use different pair (ω，ω_p) during PSO search for Rosenbrock function. With this modification the dimension can be set over 200. In this thesis, five benchmark optimization problems have been selected for demonstration. These five functions are Sphere, Griewank, Quatric, Rastrigin, and Rosenbrock. Different setting for PSO parameters and two mutation mechanisms compose a specific PSO algorithm for each function. We have found that with correct setting of parameters and mechanisms, the final dimension can increased as high as 1000. Key Words : Particle Swarm Optimization, Mutation, One-variable Mutation, Optimization function
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Fan, Guo-Wei, and 范國瑋. "Dynamic Simulation and Analysis for 1D Extension Forming and Optimization of Parameters Setting." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/pkpr4s.

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碩士<br>國立臺灣科技大學<br>機械工程系<br>94<br>This research is aimed to develop a computer code that can provide the user a test setting and eventually advance to an optimal parameters setup for a 1D extension system. Via deriving the equations of motion for an extension system, a computer program was developed for test run. The user only needs to provide the system configuration and material parameters, and the program will output the tension and stress distribution as the first check up. A tension analysis follows to give the user an initial outline of extension setup. Modifications hence can be proceeded if necessary. The processing of Aluminium and PVA extension forming were illustrated as examples. Various different object functions and optimal criteria were then demonstrated accordingly. To obtain the optimal parameters, the Genetic Algorithm method was employed. This computer code is well developed and proven and it is believed to provide both the analysis and design engineers a useful tool for setting the extension process.
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Book chapters on the topic "Parameter setting optimization"

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Yang, Yi, Xi Chen, Ningyun Lu, and Furong Gao. "Parameter Setting for the Plastication Stage." In Injection Molding Process Control, Monitoring, and Optimization. Carl Hanser Verlag GmbH & Co. KG, 2016. http://dx.doi.org/10.3139/9781569905937.011.

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Choi, Young Hwan, Sajjad Eghdami, Thi Thuy Ngo, Sachchida Nand Chaurasia, and Joong Hoon Kim. "Comparison of Parameter-Setting-Free and Self-adaptive Harmony Search." In Harmony Search and Nature Inspired Optimization Algorithms. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_11.

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Jagatheesan, K., B. Anand, Soumadip Sen, Swarnavo Mondal, and Sourav Samanta. "Automatic Generation Control Scheme for Power Quality Improvement of Multi-source Power Generating System with Secondary Controller Optimization Using Parameter-Setting-Free Harmony Search." In Springer Tracts in Nature-Inspired Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6195-9_2.

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Cuevas, Erik, Jorge Gálvez, and Omar Avalos. "Optimization Techniques in Parameters Setting for Induction Motor." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28917-1_2.

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Fidanova, Stefka, Olympia Roeva, and Maria Ganzha. "ACO and GA for Parameter Settings of E. coli Fed-Batch Cultivation Model." In Recent Advances in Computational Optimization. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00410-5_4.

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Rafael, Brigitte, Stefan Oertl, Michael Affenzeller, and Stefan Wagner. "Optimization of Parameter Settings for Genetic Algorithms in Music Segmentation." In Computer Aided Systems Theory – EUROCAST 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27549-4_31.

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Fraccaroli, Michele, Evelina Lamma, and Fabrizio Riguzzi. "Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_43.

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Holzinger, Emily R., Scott M. Dudek, Eric C. Torstenson, and Marylyn D. Ritchie. "ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models." In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20389-3_5.

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Al-Obeidat, Feras, Nabil Belacel, Juan A. Carretero, and Prabhat Mahanti. "Automatic Parameter Settings for the PROAFTN Classifier Using Hybrid Particle Swarm Optimization." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13059-5_19.

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Wu, Ai-hua, and Zhi-yuan Ma. "Research on the Production Logistic Parameters Setting and Simulating Optimization in Discrete Manufacturing Enterprise." In Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40063-6_86.

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Conference papers on the topic "Parameter setting optimization"

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Long, Zalizah Awang, Abdul Razak Hamdan, and Azuraliza Abu Bakar. "Parameter setting procedure via quick parameter evaluation in frequent pattern mining for outbreak detection." In 2009 2nd Conference on Data Mining and Optimization. IEEE, 2009. http://dx.doi.org/10.1109/dmo.2009.5341905.

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Zielinski, Karin, and Rainer Laur. "Differential evolution with adaptive parameter setting for multi-objective optimization." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424937.

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Tamura, Kenichi, and Keiichiro Yasuda. "A Stability Analysis Based Parameter Setting Method for Spiral Optimization." In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.667.

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Fan, Zhun, Jie Ruan, Wenji Li, et al. "A Learning Guided Parameter Setting for Constrained Multi-Objective Optimization." In 2019 1st International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2019. http://dx.doi.org/10.1109/iciai.2019.8850786.

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Zielinski, Karin, and Rainer Laur. "Adaptive parameter setting for a multi-objective particle swarm optimization algorithm." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424856.

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Zhixiong Liu. "Empirical study of the random number parameter setting for particle swarm optimization algorithm." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645320.

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Tonbul, Hasan, and Taskln Kavzaoglu. "Application of Taguchi Optimization and ANOVA Statistics in Optimal Parameter Setting of Multi-Resolution Segmentation." In 2019 9th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2019. http://dx.doi.org/10.1109/rast.2019.8767784.

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Rashed, G. I., H. I. Shaheen, and S. J. Cheng. "Optimal Location and Parameter Setting of TCSC by Both Genetic Algorithm and Particle Swarm Optimization." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318586.

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Wang, Shuo, Hongliang Yu, Shizeng Lu, Xiaohong Wang, and Huaguo Liu. "Application of Least Square Support Vector Machine with Adaptive Particle Swarm Parameter Optimization in Grate Pressure Optimization Setting of Grate Cooler." In 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2020. http://dx.doi.org/10.1109/yac51587.2020.9337619.

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Puri, Y. M., and N. V. Deshpande. "Parametric Optimization of WEDM of High Chromium High Carbon Die Steel Using ANN." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14306.

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Abstract:
In this research, single pass cutting of HCHC (High Chromium High Carbon) die steel AISI D3 Grade has been used where material removal rate (MRR) and surface roughness (SR) are of primary importance. In general, achieving a high level cutting speed with a better surface finish is extremely difficult task because in wire cut electric discharge machining (WEDM), no particular parametric combination is expected to yield simultaneously in the best MRR and the best SR. Hence it can be considered as multi objective optimization problem. This research presents an attempt at multi objective optimization of the process parametric combinations by modeling the process using artificial neural network (ANN). A feed forward back propagation neural network based on matrix experimental design is developed to model the WEDM process. Based on the developed model, different response parameters are calculated for various input parameter setting. Finally they are compared to find out optimal combination of machining parameter setting. Electronica make EZEECUT PLUS model has been used for experimentation and a methodology has been suggested to determine the optimal combination of control parameters in WEDM. Research findings in the area of machining HCHC die steel through WEDM process will open up a new horizon and will certainly solve various challenging problems faced by the engineers and die makers in the field of modern manufacturing industry.
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Reports on the topic "Parameter setting optimization"

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Rahman, Shahedur, Rodrigo Salgado, Monica Prezzi, and Peter J. Becker. Improvement of Stiffness and Strength of Backfill Soils Through Optimization of Compaction Procedures and Specifications. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317134.

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Vibration compaction is the most effective way of compacting coarse-grained materials. The effects of vibration frequency and amplitude on the compaction density of different backfill materials commonly used by INDOT (No. 4 natural sand, No. 24 stone sand, and No. 5, No. 8, No. 43 aggregates) were studied in this research. The test materials were characterized based on the particle sizes and morphology parameters using digital image analysis technique. Small-scale laboratory compaction tests were carried out with variable frequency and amplitude of vibrations using vibratory hammer and vibratory table. The results show an increase in density with the increase in amplitude and frequency of vibration. However, the increase in density with the increase in amplitude of vibration is more pronounced for the coarse aggregates than for the sands. A comparison of the maximum dry densities of different test materials shows that the dry densities obtained after compaction using the vibratory hammer are greater than those obtained after compaction using the vibratory table when both tools were used at the highest amplitude and frequency of vibration available. Large-scale vibratory roller compaction tests were performed in the field for No. 30 backfill soil to observe the effect of vibration frequency and number of passes on the compaction density. Accelerometer sensors were attached to the roller drum (Caterpillar, model CS56B) to measure the frequency of vibration for the two different vibration settings available to the roller. For this roller and soil tested, the results show that the higher vibration setting is more effective. Direct shear tests and direct interface shear tests were performed to study the impact of particle characteristics of the coarse-grained backfill materials on interface shear resistance. The more angular the particles, the greater the shear resistance measured in the direct shear tests. A unique relationship was found between the normalized surface roughness and the ratio of critical-state interface friction angle between sand-gravel mixture with steel to the internal critical-state friction angle of the sand-gravel mixture.
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