This article focuses on the objective functions and models of corporate optimization. The problems of formulation of objectives have given rise to new terminology. For example, the phrase 'objective function' ...
详细信息
This article focuses on the objective functions and models of corporate optimization. The problems of formulation of objectives have given rise to new terminology. For example, the phrase 'objective function' has made its way into the literature of economics, operations research and management science by the route of linear programming. In a linear program the objective function is the expression to be maximized or minimized. The typical programming problem is one in which the prices of the final products are given, the capacity limitations on production are specified, technology is assumed to be known and the goal is to maximize a revenue or equivalently, minimize a cost subject to constraints. Unfortunately in problems involving more than a few aspects of the meaning of a complex organization such as the modern firm, it becomes most difficult to describe the objective function. As the complexity of the organization grows the so-called objective function becomes more and more subjective and less quantitative as multiple goals, social and political goals and uncertainty and ill-perception of the environment are taken into account.
Three different hydrological models are chosen to simulate rainfall-runoff relationships under each of three objective functions including mean squared errors of squared transformed flows, squared root transformed flo...
详细信息
Three different hydrological models are chosen to simulate rainfall-runoff relationships under each of three objective functions including mean squared errors of squared transformed flows, squared root transformed flows and logarithmic transformed flows;thus nine individual models are constructed. By weighted averaging over these nine models, the method of Bayesian model averaging (BMA) was used to provide both the mean value and the uncertainty intervals of flow prediction. Three kinds of uncertainty information can be generated: the uncertainty of individual member model's predictions;the total uncertainty of BMA mean prediction;the between-model and within-model uncertainties in the BMA scheme. Based on the estimated results in this study, the coupling of multiple models with multiple objective functions in general offers better results for both the mean prediction and the uncertainty intervals for the runoffs in a selected basin in Han River, China, than the individual models.
objective functions provide measurements of solution quality that represent the core calculations required to tackle NP-hard optimization problems. Since their complexity keeps growing with the introduction of more re...
详细信息
objective functions provide measurements of solution quality that represent the core calculations required to tackle NP-hard optimization problems. Since their complexity keeps growing with the introduction of more realistic data, research efforts have turned their interest into the proposal of efficient objective function implementations that take advantage of potential parallelism. This work explores GPGPU technologies to accelerate objective functions, considering as a case study the parallelization of phylogenetic parsimony calculations from DNA data. We undertake the comparative evaluation of different GPU programming models and architectures, highlighting the benefits and drawbacks of each approach through experimentation on six real-world biological datasets. Experimental results shed light on the strong relationship between the characteristics of the input data and the effective utilization of GPU resources. Furthermore, comparisons with other parallel architectures and methods point out how current and future optimization scenarios can benefit from the use of accurate, efficient GPU approaches. (C) 2018 Elsevier Inc. All rights reserved.
In recent studies, the suspension parameters of a vehicle model are estimated using multi-objective optimization procedures with genetic algorithms in order to overcome the well known conflict of ride comfort and road...
详细信息
In recent studies, the suspension parameters of a vehicle model are estimated using multi-objective optimization procedures with genetic algorithms in order to overcome the well known conflict of ride comfort and road holding. However, the researchers sometimes end up using more than one objective function representing the same requirement, growing the dimension of the optimization problem. Thus, the optimization procedure becomes very quickly ineffective and the merits of the GAs are put aside because of the increased computational time of the simulations. This work focuses on indicating that the inconsiderate selection of objective functions noticed in the literature, in order to obtain the optimum solution of a suspension design, doesn't lead to extra quality in the solution. In this direction, six objective functions widely used in the literature depicting the ride comfort and the road holding, were selected. In our experiments, various SOO approaches (Part A) and two MOO approaches (Part B and C) were selected, where Part B is proposing a novel way of handling the optimization objectives. All the MOO approaches presented combine GAs for obtaining the Pareto set and a sorting algorithm for pointing out their optimum solution among the Pareto alternatives. The optimum solutions of the two approaches are presented and compared in terms of convergence and computational time, concluding to the fact that the economy in the objective functions could provide not only a better solution but also could save significant computational time. (C) 2018 Elsevier Ltd. All rights reserved.
Optimisation techniques have been developed and used to determine material constants arising in unified creep/viscoplastic constitutive equations based on experimental data. objective functions (OF) have been formulat...
详细信息
Optimisation techniques have been developed and used to determine material constants arising in unified creep/viscoplastic constitutive equations based on experimental data. objective functions (OF) have been formulated as pointers to the quality of fit between the equations and experimental data and a set of criteria is presented to assess the suitability of the objective functions. Convergence features of two existing objective functions are analysed. The problems of using these objective functions are studied. To overcome difficulties arising in the existence of different scales of individual sub-objectives, a novel error definition is introduced, which has a natural unitless form and can provide a measure for "true" error. A novel weighting technique is introduced, which can also be chosen automatically to compensate the loss of credits in individual data points and curves. Using these techniques, a novel objective function is formulated, which meets the set criteria. The objective function, together with an evolutionary programming (EP) solver, are employed to determine material constants in three sets of unified constitutive equations which are formulated to match experimental data. The convergent features of the objective functions are compared and analysed. (C) 2007 Elsevier Ltd. All rights reserved.
This paper gives a combinatorial proof of a ''yes'' answer to an open question presented in [1], stated as follows: ''given a multilinear polynomial E(x): [0, 1](n) --> R, is it true that E-...
详细信息
This paper gives a combinatorial proof of a ''yes'' answer to an open question presented in [1], stated as follows: ''given a multilinear polynomial E(x): [0, 1](n) --> R, is it true that E-b(x) = E(x) - b(t)z has a strict local minimum over the discrete set {0,1}(n) for almost all b of sufficiently small norm?'' The given combinatorial proof is completed directly by providing a sufficient condition for a conjecture on the strict local minima of multilinear polynomials also postulated in [1] to hold. In addition, a simple counterexample is presented to demonstrate that the conjecture may be not true if the provided sufficient condition is not satisfied.
A mathematical program with a maximin objective function is defined as an optimization problem of the following type: Maxz = mini cixi, subject to AX = b, X ≧ 0. Although the ci can be in the interval (? ∞, ∞), the...
详细信息
A mathematical program with a maximin objective function is defined as an optimization problem of the following type: Maxz = mini cixi, subject to AX = b, X ≧ 0. Although the ci can be in the interval (? ∞, ∞), the paper discusses the more common practical case where all ci ≧ 0. It shows that problems of this type arise in a variety of applications where it is required to maximize a production function of the “fixed proportion” type subject to a set of linear constraints. Although it is well known that the solution to this type of problem can be found by linear programming, this paper shows that, if the existence of a certain condition can be demonstrated, then a simplified method can be used to determine the optimum solution. Many problems of practical interest can be solved by this simplified method; an example involving the readiness of a ship is presented.
Reliable quantification of groundwater recharge rate is crucial for the sustainable utilization of groundwater resources. However, little information is documented about the uncertainty associated with recharge rate e...
详细信息
Reliable quantification of groundwater recharge rate is crucial for the sustainable utilization of groundwater resources. However, little information is documented about the uncertainty associated with recharge rate estimation from the different combinations of model complexity and objective function perspectives. Therefore, this study aims to (i) analyze the sensitivity of the model parameters under different combinations of model complexities and objective functions and (ii) estimate the groundwater recharge rate in the Hombele catchment, Upper Awash Basin, Ethiopia, for different combinations of objective functions and model complexities. The effect of these model complexities in estimating groundwater recharge rate and parametrizing model parameters was quantified for the period 1986-2013. A total of 10 combinations of model complexities and objective functions were used for the analysis. The Kling-Gupta efficiency (Nash-Sutcliffe efficiency) values for calibration, validation, and the whole period are 0.89 (0.80), 0.80 (0.73), and 0.87 (0.77), respectively, when a semi-distributed HBV-light model was used. For all objective functions, we found that the average annual recharge rate of the study catchment ranges from 185.9 to 280.5 mm when the HBV-light model was considered as a semi-distributed model. In contrast, the average annual recharge rate ranges from 185.3 to 321.7 mm when applying the HBV-light model as a lumped model.
Backpropagation, similar to most learning algorithms that can form complex decision Surfaces, is prone to overfitting. This work presents classification-based objective functions, an approach to training artificial ne...
详细信息
Backpropagation, similar to most learning algorithms that can form complex decision Surfaces, is prone to overfitting. This work presents classification-based objective functions, an approach to training artificial neural networks on classification problems. Classification-based learning attempts to guide the network directly to correct pattern classification rather than using common error minimization heuristics, such as sum-squared error (SSE) and cross-entropy (CE), that do not explicitly minimize classification error. CBI is presented here as a novel objective function for learning classification problems. It seeks to directly minimize classification error by backpropagating error only on misclassified patterns from culprit Output nodes. CB1 discourages weight saturation and overfitting and achieves higher accuracy on classification problems than optimizing SSE or CE. Experiments on a large OCR data set have shown CB1 to significantly increase generalization accuracy over SSE or CE optimization, from 97.86% and 98.10%, respectively, to 99.11%. Comparable results are achieved over several data sets from the UC Irvine Machine Learning Database Repository, with an average increase in accuracy from 90.7% and 91.3% using optimized SSE and CE networks, respectively, to 92.1% for CB1. Analysis indicates that CBI performs a fundamentally different search of the feature space than optimizing SSE or CE and produces significantly different solutions.
Multi-Layer Neural Networks (MLNNs) have been known to be used to model the statistical properties of their training data. Several authors have shown that, depending on the objective function chosen, MLNNs estimate th...
详细信息
Multi-Layer Neural Networks (MLNNs) have been known to be used to model the statistical properties of their training data. Several authors have shown that, depending on the objective function chosen, MLNNs estimate the posterior class probabilities of their inputs, provided the network is trained with binary desired outputs. If has recently been shown that conditions exist that define a general class of objective functions which provide probability estimates. This paper introduces a method of generating such objective functions. This generator is simple to use, and so far has been found to be universally applicable. Known objective functions, which include the mean-squared error (MSE) and the cross entropy (CE) measure, are generated here as examples of its application. To demonstrate the potential of this method a new objective function is derived and discussed. This work provides practising engineers with an explicit method for generating objective functions that could be used in their classification applications. Copyright (C) 1996 Elsevier Science Ltd
暂无评论