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Relevant bibliographies by topics / Optimization problem setting

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Academic literature on the topic 'Optimization problem setting'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Optimization problem setting"

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Chen, Qun. "Global Optimization for Bus Line Timetable Setting Problem." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/636937.

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This paper defines bus timetables setting problem during each time period divided in terms of passenger flow intensity; it is supposed that passengers evenly arrive and bus runs are set evenly; the problem is to determine bus runs assignment in each time period to minimize the total waiting time of passengers on platforms if the number of the total runs is known. For such a multistage decision problem, this paper designed a dynamic programming algorithm to solve it. Global optimization procedures using dynamic programming are developed. A numerical example about bus runs assignment optimizatio

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Liu, Haoxiang, David Z. W. Wang, and Hao Yue. "Global Optimization for Transport Network Expansion and Signal Setting." Mathematical Problems in Engineering 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/385713.

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This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model wi

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Azimi, Javad, Xiaoli Fern, and Alan Fern. "Budgeted Optimization with Constrained Experiments." Journal of Artificial Intelligence Research 56 (May 30, 2016): 119–52. http://dx.doi.org/10.1613/jair.4896.

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Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(.) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x)

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Kumar, Ashok V. "A Sequential Optimization Algorithm Using Logarithmic Barriers: Applications to Structural Optimization." Journal of Mechanical Design 122, no. 3 (1999): 271–77. http://dx.doi.org/10.1115/1.1288363.

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A sequential approximation algorithm is presented here that is particularly suited for problems in engineering design and structural optimization, where the number of variables is very large and function and sensitivity evaluations are computationally expensive. A sequence of sub-problems are generated using a linear approximation for the objective function and setting move limits on the variables using a barrier method. These sub-problems are strictly convex and computation per iteration is significantly reduced by not solving the sub-problems exactly. Instead a few Newton-steps are taken for

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Adacher, Ludovica, and Andrea Gemma. "A robust algorithm to solve the signal setting problem considering different traffic assignment approaches." International Journal of Applied Mathematics and Computer Science 27, no. 4 (2017): 815–26. http://dx.doi.org/10.1515/amcs-2017-0057.

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AbstractIn this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads

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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 c

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LIN, HONGCAN, DAVID SAUNDERS, and CHENGGUO WENG. "PORTFOLIO OPTIMIZATION WITH PERFORMANCE RATIOS." International Journal of Theoretical and Applied Finance 22, no. 05 (2019): 1950022. http://dx.doi.org/10.1142/s0219024919500225.

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We consider the portfolio selection problem of maximizing a performance measure in a continuous-time diffusion model. The performance measure is the ratio of the overperformance to the underperformance of a portfolio relative to a benchmark. Following a strategy from fractional programming, we analyze the problem by solving a family of related problems, where the objective functions are the numerator of the original problem minus the denominator multiplied by a penalty parameter. These auxiliary problems can be solved using the martingale method for stochastic control. The existence of solutio

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Harsha, Pavithra, Ramesh Natarajan, and Dharmashankar Subramanian. "A Prescriptive Machine-Learning Framework to the Price-Setting Newsvendor Problem." INFORMS Journal on Optimization 3, no. 3 (2021): 227–53. http://dx.doi.org/10.1287/ijoo.2019.0046.

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The approach to data-driven optimization described in this paper was developed when the authors were part of an IBM project team working with the U.S. Department of Energy, Pacific National Laboratory, and various energy utility partners on an initiative to develop a smart energy distribution infrastructure. Within this broader scope and based on the data collected in some initial controlled experiments, the paper specifically addresses the design and optimization of real-time price incentives to consumers to manage their electricity demand and determine the energy capacity to be provisioned b

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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 i

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Ezeora, Jeremiah, and Chinedu Izuchukwu. "Iterative approximation of solution of split variational inclusion problem." Filomat 32, no. 8 (2018): 2921–32. http://dx.doi.org/10.2298/fil1808921e.

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Following recent important results of Moudafi [Journal of Optimization Theory and Applications 150(2011), 275-283] and other related results on variational problems, we introduce a new iterative algorithm for approximating a solution of monotone variational inclusion problem involving multi-valued mapping. The sequence of the algorithm is proved to converge strongly in the setting of Hilbert spaces. As application, we solved split convex optimization problems.

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Dissertations / Theses on the topic "Optimization problem setting"

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Mohiuddin, Mohammed Aijaz. "Engineering Nature-Inspired Heuristics for the Open Shortest Path First Weight Setting Problem." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/65988.

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In the thesis of “Mohammed Aijaz Mohiuddin”, Engineering Nature-Inspired Heuristics for the Open Shortest Path First Weight Setting Problem, nature inspired heuristics were developed. Besides the existing two objectives, namely maximum utilization and the number of congested links, a third objective namely the number of unused links was used to formulate the fuzzy based objective function for the OSPFWS problem. The idea was to make use unused network links if any. Furthermore, a hybrid fuzzy based evolutionary Particle Swarm Optimization (FEPSO) algorithm was designed that harnessed evolution

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Konečný, Jakub. "Stochastic, distributed and federated optimization for machine learning." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31478.

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We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that enables linear convergence for strongly convex objectives. Second, we study distributed setting, in which the data describing the optimization problem does not fit into a single computing node. In this case, traditional methods are inefficient, as the communication costs inherent in distributed optimization become the bottleneck. We propose a communication-efficient framework whi

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Hadbi, Djamel. "Formulations de problèmes d’optimisation multiniveaux pour la conception de réseaux de bord électriques en aéronautique." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT115/document.

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Dans le contexte de l’avion plus électrique, les réseaux électriques aéronautiques sont en pleine évolution. Cette évolution est poussée par le besoin d’une intégration à forte densité énergétique ce qui pose des défis aux concepteurs en termes d’architectures, de systèmes et de méthodes de dimensionnement.Un réseau de bord est composé d’un ensemble de systèmes électriques multidisciplinaire qui proviennent de différents fournisseurs dont le design est actuellement effectué en répondant à des standards de qualité spécifiés par l’agrégateur. L’objectif de la thèse est de proposer de nouvelles a

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Kanade, Gaurav Nandkumar. "Combinatorial optimization problems in geometric settings." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1152.

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We consider several combinatorial optimization problems in a geometric set- ting. The first problem we consider is the problem of clustering to minimize the sum of radii. Given a positive integer k and a set of points with interpoint distances that satisfy the definition of being a "metric", we define a ball centered at some input point and having some radius as the set of all input points that are at a distance smaller than the radius of the ball from its center. We want to cover all input points using at most k balls so that the sum of the radii of the balls chosen is minimized. We show that

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Danniels, Travis. "Deblurring with Framelets in the Sparse Analysis Setting." Thesis, 2013. http://hdl.handle.net/1828/5107.

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In this thesis, algorithms for blind and non-blind motion deblurring of digital images are proposed. The non-blind algorithm is based on a convex program consisting of a data fitting term and a sparsity-promoting regularization term. The data fitting term is the squared l_2 norm of the residual between the blurred image and the latent image convolved with a known blur kernel. The regularization term is the l_1 norm of the latent image under a wavelet frame (framelet) decomposition. This convex program is solved with the first-order primal-dual algorithm proposed by Chambolle and Pock. T

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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 furthe

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Books on the topic "Optimization problem setting"

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1943-, Gossez J. P., and Bonheure Denis, eds. Nonlinear elliptic partial differential equations: Workshop in celebration of Jean-Pierre Gossez's 65th birthday, September 2-4, 2009, Université libre de Bruxelles, Belgium. American Mathematical Society, 2011.

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Chambers, Robert G. Competitive Agents in Certain and Uncertain Markets. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190063016.001.0001.

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This book uses concepts from optimization theory to develop an integrated analytic framework for treating consumer, producer, and market equilibrium analyses as special cases of a generic optimization problem. The same framework applies to both stochastic and non-stochastic decision settings, so that the latter is recognized as an (important) special case of the former. The analytic techniques are borrowed from convex analysis and variational analysis. Special emphasis is given to generalized notions of differentiability, conjugacy theory, and Fenchel's Duality Theorem. The book shows how virt

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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results

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Golan, Amos. Foundations of Info-Metrics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.001.0001.

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This book provides a framework for info-metrics—the science of modeling, inference, and reasoning under conditions of noisy and insufficient information. Info-metrics is an inherently interdisciplinary framework that emerged from the intersection of information theory, statistical inference, and decision-making under uncertainty. It allows us to process the available information with minimal reliance on assumptions that cannot be validated. This book focuses on unifying all information processing and model building within a single constrained optimization framework. It provides a complete fram

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Book chapters on the topic "Optimization problem setting"

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Szabó, P. G., T. Csendes, L. G. Casado, and I. García. "Packing Equal Circles in a Square I. — Problem Setting and Bounds for Optimal Solutions." In Applied Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4613-0295-7_14.

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Korf, Lisa A. "An Approximation Framework for Infinite Horizon Optimization Problems in a Mathematical Programming Setting." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57014-8_13.

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Tiozzo, S., W. Battaglia, and A. Migatta. "Optimization of Combustion Settings: An Energy Efficient Approach to the Reduction of Emissions by Glass Melting Furnaces." In 77th Conference on Glass Problems. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119417507.ch10.

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Polprasert, Jirawadee, Weerakorn Ongsakul, and Vo Ngoc Dieu. "Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem." In Sustaining Power Resources through Energy Optimization and Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9755-3.ch008.

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This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudo-gradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.

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Kumar, Rajeev. "Algorithms for Selecting the Optimum Dataset While Providing Personalized Privacy and Compensation to its Participants." In Research Anthology on Privatizing and Securing Data. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8954-0.ch033.

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The privacy preserving microdata sharing literature has proposed several techniques that allow a database administrator to share a dataset in a privacy preserving manner. This paper considers the implications of adding a market layer to that setting. In this setting, individuals (data providers) can receive a market-determined compensation in exchange for their information while they also receive a personalized privacy protection. The computational burdens of satisfying a variety of privacy requirements of individuals (sellers) and dataset requirements of the data receiver (buyer) are analyzed in this paper. The author presents a polynomial time reformulation procedure that proves that the “optimum information product” creation problem reduces to multiple-choice knapsack problem, which is a weakly NP hard problem. The problem of various instance sizes is solved using FICO Xpress 7.0 optimization software. The insights presented in the paper can be utilized for creating a market of individual information in different settings.

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Ries, Jana, Patrick Beullens, and Yang Wang. "Instance-Specific Parameter Tuning for Meta-Heuristics." In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2086-5.ch005.

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Meta-heuristics are of significant interest to decision-makers due to the capability of finding good solutions for complex problems within a reasonable amount of computational time. These methods are further known to perform according to how their algorithm-specific parameters are set. As most practitioners aim for an off-the-shelf approach when using meta-heuristics, they require an easy applicable strategy to calibrate its parameters and use it. This chapter addresses the so-called Parameter Setting Problem (PSP) and presents new developments for the Instance-specific Parameter Tuning Strategy (IPTS). The IPTS presented only requires the end user to specify its preference regarding the trade-off between running time and solution quality by setting one parameter p (0 = p =1), and automatically returns a good set of algorithm-specific parameter values for each individual instance based on the calculation of a set of problem instance characteristics. The IPTS does not require any modification of the particular meta-heuristic being used. It aims to combine advantages of the Parameter Tuning Strategy (PTS) and the Parameter Control Strategy (PCS), the two major approaches to the PSP. The chapter outlines the advantages of an IPTS and shows in more detail two ways in which an IPTS can be designed. The first design approach requires expert-based knowledge of the meta-heuristic’s performance in relation to the problem at hand. The second, automated approach does not require explicit knowledge of the meta-heuristic used. Both designs use a fuzzy logic system to obtain parameter values. Results are presented for an IPTS designed to solve instances of the Travelling Salesman Problem (TSP) with the meta-heuristic Guided Local Search (GLS).

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Tsamoura, Efthymia, Anastasios Gounaris, and Yannis Manolopoulos. "Optimal Service Ordering in Decentralized Queries Over Web Services." In Grid and Cloud Computing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0879-5.ch613.

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The problem of ordering expensive predicates (or filter ordering) has recently received renewed attention due to emerging computing paradigms such as processing engines for queries over remote Web Services, and cloud and grid computing. The optimization of pipelined plans over services differs from traditional optimization significantly, since execution takes place in parallel and thus the query response time is determined by the slowest node in the plan, which is called the bottleneck node. Although polynomial algorithms have been proposed for several variants of optimization problems in this setting, the fact that communication links are typically heterogeneous in wide-area environments has been largely overlooked. The authors propose an attempt to optimize linear orderings of services when the services communicate directly with each other and the communication links are heterogeneous. The authors propose a novel optimal algorithm to solve this problem efficiently. The evaluation of the proposal shows that it can result in significant reductions of the response time.

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Ganesan, T., Pandian Vasant, and I. Elamvazuthi. "Multiobjective Optimization of Solar-Powered Irrigation System with Fuzzy Type-2 Noise Modelling." In Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0914-1.ch008.

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Design optimization has been commonly practiced for many years across various engineering disciplines. Optimization per se is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. Therefore, successful modelling and optimization procedures would require a framework that encompasses all these uncertainty features and solves the problem at hand with reasonable accuracy. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.

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"Modeling and Optimization of Parabolic Trough Collector." In Modeling and Optimization of Solar Thermal Systems. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3523-3.ch005.

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Parabolic trough collector (PTC) is a concentrating collector widely used for steam cooking, water heating, and also steam power generation and desalination work. The performance of PTC is strongly depends on its process parameters and is a MCDM problem. Implementation of integrated method, that is, entropy with graph theory and matrix approach (E-GTMA) for modelling and optimization of PTC parameters to improve higher outlet temperature (To), higher heat gain (h), and higher thermal efficiency (ηth), is discussed in this chapter. Investigation results indicate the effectiveness of this technique for multi-objective optimization and determined optimal setting as Test no.10 for PTC. Additionally, parametric and ANOVA analysis is carried out to determine the significance and adequacy of the developed model. Last, validation of the proposed model and verification results is done via confirmatory tests, and tests results show comparable and acceptable w.r.t. experimental results.

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Hirsch, Patrick. "Minimizing Empty Truck Loads in Round Timber Transport with Tabu Search Strategies." In Management Innovations for Intelligent Supply Chains. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2461-0.ch006.

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In Central Europe transportation accounts for an estimated 30% of the total costs of round timber and is an essential element of cost. In this paper, Tabu Search based methods for solving the Timber Transport Vehicle Routing Problem (TTVRP) are presented. The TTVRP uses solutions of preceding optimization problems as input that are obtained with standard solver software. The presented methods differ with respect to the considered neighborhood and the static or dynamic setting of their parameters. They are verified with real life data in extensive numerical studies and compared to the results obtained with the solver software Xpress. It is shown that the methods are capable to generate good solutions in reasonable computing time.

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Conference papers on the topic "Optimization problem setting"

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Yang, Kang, and Qi Yang. "Optimization Study on Pump Series Setting of Deep-Sea Mining Pipeline." In ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/omae2012-83708.

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Along with the running up of land resources, deep sea exploitation has been important strategic goal of big countries of the world, there are significant meanings to realize efficient and low-energy-consumption piping of deep sea mine. Base on the vertical piping theory of large particles and theory of centrifugal pump, operation points are solved in different working conditions with provided pump series. Consider the problem on the optimization indexes of efficiency, production, power and energy consumption per unit production, conduct analysis and make comparison according to diagrams, optim

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Gu, Bin, Wenhan Xian, and Heng Huang. "Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/104.

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Asynchronous parallel stochastic optimization for non-convex problems becomes more and more important in machine learning especially due to the popularity of deep learning. The Frank-Wolfe (a.k.a. conditional gradient) algorithms has regained much interest because of its projection-free property and the ability of handling structured constraints. However, our understanding of asynchronous stochastic Frank-Wolfe algorithms is extremely limited especially in the non-convex setting. To address this challenging problem, in this paper, we propose our asynchronous stochastic Frank-Wolfe algorithm (A

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Curely, J., and A. Mokrani. "Theory of Superexchange for 3dⁿ-Ions (1≤n≤9) involved in natural and artificial magnets I — setting the problem." In 2008 11th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2008. http://dx.doi.org/10.1109/optim.2008.4602335.

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Benabid, Rabah. "A new formulation and solving of protective relays setting and coordination problem using multi-objective optimization and fuzzy logic." In 2016 8th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2016. http://dx.doi.org/10.1109/icmic.2016.7804288.

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Chu, Y. X., K. Cao, H. Wu, and Z. X. Li. "On the Hybrid Workpiece Localization/Inspection/Machinability Problem." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dfm-8914.

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Abstract This work addresses the localization, inspection and machinability problem of a workpiece. It includes the problem of aligning the CAD model of a workpiece such that all points measured on the finished surfaces of the workpiece match closely to corresponding surfaces on the model with minimum zone tolerances while all unmachined surfaces lie outside the model and have the maximal machinable volume. This is referred to as the hybrid localization/inspection/machinability problem and has important applications in setting up and inspecting of partially finished workpieces. This paper firs

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Yoshimura, Masataka, Kazuhiro Izui, and Shigeaki Komori. "Optimization of Machine System Designs Using Hierarchical Decomposition Based on Criteria Influence." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34042.

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Machine product designs routinely have so many mutually related characteristics that common design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on the clarification of the conflicting and cooperative relationships among the characteristics. First of all, each performance characteristic is divided into simpler basic characteristics according to its structure. Next, the relationships among the basic characteristics are systematically identified and clarified. Then, based on

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Kumar, Ashok V., and David C. Gossard. "A Sequential Approximation Method for Structural Optimization Using Logarithmic Barriers." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0366.

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Abstract A sequential approximation technique for non-linear programming is presented here that is particularly suited for problems in engineering design and structural optimization, where the number of variables are very large and function and sensitivity evaluations are computationally expensive. A sequence of sub-problems are iteratively generated using a linear approximation for the objective function and setting move limits on the variables using a barrier method. These sub-problems are strictly convex. Computation per iteration is significantly reduced by not solving the sub-problems exa

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Xu, Meng, Georges Fadel, and Margaret M. Wiecek. "Dual Residual in Augmented Lagrangian Coordination for Decomposition-Based Optimization." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35103.

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As system design problems increase in complexity, researchers seek approaches to optimize such problems by coordinating the optimizations of decomposed sub-problems. Many methods for optimization by decomposition have been proposed in the literature among which, the Augmented Lagrangian Coordination (ALC) method has drawn much attention due to its efficiency and flexibility. The ALC method involves a quadratic penalty term, and the initial setting and update strategy of the penalty weight are critical to the performance of the ALC. The weight in the traditional weight update strategy always in

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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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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 optimizati

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Arrighi, Emmanuel, Henning Fernau, Daniel Lokshtanov, Mateus de Oliveira Oliveira, and Petra Wolf. "Diversity in Kemeny Rank Aggregation: A Parameterized Approach." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/2.

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In its most traditional setting, the main concern of optimization theory is the search for optimal solutions for instances of a given computational problem. A recent trend of research in artificial intelligence, called solution diversity, has focused on the development of notions of optimality that may be more appropriate in settings where subjectivity is essential. The idea is that instead of aiming at the development of algorithms that output a single optimal solution, the goal is to investigate algorithms that output a small set of sufficiently good solutions that are sufficiently diverse f

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