3,769 research outputs found

    Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

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    This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives; and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multi-objective optimization, genetic algorithms and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics

    Metaheuristic design of feedforward neural networks: a review of two decades of research

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    Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

    On multiobjective optimization from the nonsmooth perspective

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    Practical applications usually have multiobjective nature rather than having only one objective to optimize. A multiobjective problem cannot be solved with a single-objective solver as such. On the other hand, optimization of only one objective may lead to an arbitrary bad solutions with respect to other objectives. Therefore, special techniques for multiobjective optimization are vital. In addition to multiobjective nature, many real-life problems have nonsmooth (i.e. not continuously differentiable) structure. Unfortunately, many smooth (i.e. continuously differentiable) methods adopt gradient-based information which cannot be used for nonsmooth problems. Since both of these characteristics are relevant for applications, we focus here on nonsmooth multiobjective optimization. As a research topic, nonsmooth multiobjective optimization has gained only limited attraction while the fields of nonsmooth single-objective and smooth multiobjective optimization distinctively have attained greater interest. This dissertation covers parts of nonsmooth multiobjective optimization in terms of theory, methodology and application. Bundle methods are widely considered as effective and reliable solvers for single-objective nonsmooth optimization. Therefore, we investigate the use of the bundle idea in the multiobjective framework with three different methods. The first one generalizes the single-objective proximal bundle method for the nonconvex multiobjective constrained problem. The second method adopts the ideas from the classical steepest descent method into the convex unconstrained multiobjective case. The third method is designed for multiobjective problems with constraints where both the objectives and constraints can be represented as a difference of convex (DC) functions. Beside the bundle idea, all three methods are descent, meaning that they produce better values for each objective at each iteration. Furthermore, all of them utilize the improvement function either directly or indirectly. A notable fact is that none of these methods use scalarization in the traditional sense. With the scalarization we refer to the techniques transforming a multiobjective problem into the single-objective one. As the scalarization plays an important role in multiobjective optimization, we present one special family of achievement scalarizing functions as a representative of this category. In general, the achievement scalarizing functions suit well in the interactive framework. Thus, we propose the interactive method using our special family of achievement scalarizing functions. In addition, this method utilizes the above mentioned descent methods as tools to illustrate the range of optimal solutions. Finally, this interactive method is used to solve the practical case studies of the scheduling the final disposal of the spent nuclear fuel in Finland.KÀytÀnnön optimointisovellukset ovat usein luonteeltaan ennemmin moni- kuin yksitavoitteisia. Erityisesti monitavoitteisille tehtÀville suunnitellut menetelmÀt ovat tarpeen, sillÀ monitavoitteista optimointitehtÀvÀÀ ei sellaisenaan pysty ratkaisemaan yksitavoitteisilla menetelmillÀ eikÀ vain yhden tavoitteen optimointi vÀlttÀmÀttÀ tuota mielekÀstÀ ratkaisua muiden tavoitteiden suhteen. Monitavoitteisuuden lisÀksi useat kÀytÀnnön tehtÀvÀt ovat myös epÀsileitÀ siten, etteivÀt niissÀ esiintyvÀt kohde- ja rajoitefunktiot vÀlttÀmÀttÀ ole kaikkialla jatkuvasti differentioituvia. Kuitenkin monet optimointimenetelmÀt hyödyntÀvÀt gradienttiin pohjautuvaa tietoa, jota ei epÀsileille funktioille ole saatavissa. NÀiden molempien ominaisuuksien ollessa keskeisiÀ sovelluksia ajatellen, keskitytÀÀn tÀssÀ työssÀ epÀsileÀÀn monitavoiteoptimointiin. Tutkimusalana epÀsileÀ monitavoiteoptimointi on saanut vain vÀhÀn huomiota osakseen, vaikka sekÀ sileÀ monitavoiteoptimointi ettÀ yksitavoitteinen epÀsileÀ optimointi erikseen ovat aktiivisia tutkimusaloja. TÀssÀ työssÀ epÀsileÀÀ monitavoiteoptimointia on kÀsitelty niin teorian, menetelmien kuin kÀytÀnnön sovelluksien kannalta. KimppumenetelmiÀ pidetÀÀn yleisesti tehokkaina ja luotettavina menetelminÀ epÀsileÀn optimointitehtÀvÀn ratkaisemiseen ja siksi tÀtÀ ajatusta hyödynnetÀÀn myös tÀssÀ vÀitöskirjassa kolmessa eri menetelmÀssÀ. EnsimmÀinen nÀistÀ yleistÀÀ yksitavoitteisen proksimaalisen kimppumenetelmÀn epÀkonveksille monitavoitteiselle rajoitteiselle tehtÀvÀlle sopivaksi. Toinen menetelmÀ hyödyntÀÀ klassisen nopeimman laskeutumisen menetelmÀn ideaa konveksille rajoitteettomalle tehtÀvÀlle. Kolmas menetelmÀ on suunniteltu erityisesti monitavoitteisille rajoitteisille tehtÀville, joiden kohde- ja rajoitefunktiot voidaan ilmaista kahden konveksin funktion erotuksena. Kimppuajatuksen lisÀksi kaikki kolme menetelmÀÀ ovat laskevia eli ne tuottavat joka kierroksella paremman arvon jokaiselle tavoitteelle. YhteistÀ on myös se, ettÀ nÀmÀ kaikki hyödyntÀvÀt parannusfunktiota joko suoraan sellaisenaan tai epÀsuorasti. Huomattavaa on, ettei yksikÀÀn nÀistÀ menetelmistÀ hyödynnÀ skalarisointia perinteisessÀ merkityksessÀÀn. Skalarisoinnilla viitataan menetelmiin, joissa usean tavoitteen tehtÀvÀ on muutettu sopivaksi yksitavoitteiseksi tehtÀvÀksi. Monitavoiteoptimointimenetelmien joukossa skalarisoinnilla on vankka jalansija. EsimerkkinÀ skalarisoinnista tÀssÀ työssÀ esitellÀÀn yksi saavuttavien skalarisointifunktioiden perhe. Yleisesti saavuttavat skalarisointifunktiot soveltuvat hyvin interaktiivisten menetelmien rakennuspalikoiksi. TÀten kuvaillaan myös esiteltyÀ skalarisointifunktioiden perhettÀ hyödyntÀvÀ interaktiivinen menetelmÀ, joka lisÀksi hyödyntÀÀ laskevia menetelmiÀ optimaalisten ratkaisujen havainnollistamisen apuna. Lopuksi tÀtÀ interaktiivista menetelmÀÀ kÀytetÀÀn aikatauluttamaan kÀytetyn ydinpolttoaineen loppusijoitusta Suomessa

    The Kalai-Smorodinski solution for many-objective Bayesian optimization

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    An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on four problems with respectively four, six, eight, and nine objectives. The method is available in the Rpackage GPGame available on CRAN at https://cran.r-project.org/package=GPGame

    06501 Abstracts Collection -- Practical Approaches to Multi-Objective Optimization

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    From 10.12.06 to 15.12.06, the Dagstuhl Seminar 06501 ``Practical Approaches to Multi-Objective Optimization\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

    Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

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    It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

    MULTIPLE-OBJECTIVE DECISION MAKING FOR AGROECOSYSTEM MANAGEMENT

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    Multiple-objective decision making (MODEM) provides an effective framework for integrated resource assessment of agroecosystems. Two elements of integrated assessment are discussed and illustrated: (1) adding noneconomic objectives as constraints in an optimization problem; and (2) evaluating tradeoffs among competing objectives using the efficiency frontier for objectives. These elements are illustrated for a crop farm and watershed in northern Missouri. An interactive, spatial decision support system (ISDSS) makes the MODEM framework accessible to unsophisticated users. A conceptual ISDSS is presented that assesses the socioeconomic, environmental, and ecological consequences of alternative management plans for reducing soil erosion and nonpoint source pollution in agroecosystems. A watershed decision support system based on the ISDSS is discussed.Agribusiness,

    Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information

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    Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated 'physically based' watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model
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