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71.
《Journal of Multi-Criteria Decision Analysis》2017,24(1-2):25-36
We study output‐sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output‐sensitive, that is, its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of multiobjective combinatorial optimization problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions. 相似文献
72.
Michael J. Brusco 《Psychometrika》2006,71(2):347-363
Minimization of the within-cluster sums of squares (WCSS) is one of the most important optimization criteria in cluster analysis.
Although cluster analysis modules in commercial software packages typically use heuristic methods for this criterion, optimal
approaches can be computationally feasible for problems of modest size. This paper presents a new branch-and-bound algorithm
for minimizing WCSS. Algorithmic enhancements include an effective reordering of objects and a repetitive solution approach
that precludes the need for splitting the data set, while maintaining strong bounds throughout the solution process. The new
algorithm provided optimal solutions for problems with up to 240 objects and eight well-separated clusters. Poorly separated
problems with no inherent cluster structure were optimally solved for up to 60 objects and six clusters. The repetitive branch-and-bound
algorithm was also successfully applied to three empirical data sets from the classification literature. 相似文献
73.
James J. Burkitt Raoul M. Bongers Digby Elliott Steve Hansen James L. Lyons 《Journal of motor behavior》2017,49(2):129-140
Energy optimization in goal-directed aiming has been demonstrated as an undershoot bias in primary movement endpoint locations, especially in conditions where corrections to target overshoots must be made against gravity. Two-component models of upper limb movement have not yet considered how joint angles are organized to deal with the energy constraints associated with moving the upper limb in goal-directed aiming tasks. To address this limitation, participants performed aiming movements to targets in the up and down directions with the index finger and two types of rod extensions attached to the index finger. The rod extensions were expected to invoke different energy optimizing strategies in the up and down directions by allowing the distal joints the opportunity to contribute to end effector displacement. Primary movements undershot the farthest target to a greater extent in the downward direction compared to the upward direction, showing that movement kinematics optimize energy expenditure in consideration of the effects of gravity. As rod length increased, shoulder elevation was optimized in movements to the far-up target and elbow flexion was optimally minimized in movements to the far-down target. The results suggest energy optimization in the control of joint angles independent of the force of gravity. 相似文献
74.
Christopher T. Franck Haily K. Traxler Brent A. Kaplan Mikhail N. Koffarnus Mark J. Rzeszutek 《Journal of the experimental analysis of behavior》2023,119(1):156-168
Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes. 相似文献
75.
Yang Liu 《The British journal of mathematical and statistical psychology》2021,74(1):139-163
In exploratory factor analysis, latent factors and factor loadings are seldom interpretable until analytic rotation is performed. Typically, the rotation problem is solved by numerically searching for an element in the manifold of orthogonal or oblique rotation matrices such that the rotated factor loadings minimize a pre-specified complexity function. The widely used gradient projection (GP) algorithm, although simple to program and able to deal with both orthogonal and oblique rotation, is found to suffer from slow convergence when the number of manifest variables and/or the number of latent factors is large. The present work examines the effectiveness of two Riemannian second-order algorithms, which respectively generalize the well-established truncated Newton and trust-region strategies for unconstrained optimization in Euclidean spaces, in solving the rotation problem. When approaching a local minimum, the second-order algorithms usually converge superlinearly or even quadratically, better than first-order algorithms that only converge linearly. It is further observed in Monte Carlo studies that, compared to the GP algorithm, the Riemannian truncated Newton and trust-region algorithms require not only much fewer iterations but also much less processing time to meet the same convergence criterion, especially in the case of oblique rotation. 相似文献
76.
Michael J. Brusco J. Dennis Cradit Douglas Steinley 《The British journal of mathematical and statistical psychology》2020,73(3):375-396
Most partitioning methods used in psychological research seek to produce homogeneous groups (i.e., groups with low intra-group dissimilarity). However, there are also applications where the goal is to provide heterogeneous groups (i.e., groups with high intra-group dissimilarity). Examples of these anticlustering contexts include construction of stimulus sets, formation of student groups, assignment of employees to project work teams, and assembly of test forms from a bank of items. Unfortunately, most commercial software packages are not equipped to accommodate the objective criteria and constraints that commonly arise for anticlustering problems. Two important objective criteria for anticlustering based on information in a dissimilarity matrix are: a diversity measure based on within-cluster sums of dissimilarities; and a dispersion measure based on the within-cluster minimum dissimilarities. In many instances, it is possible to find a partition that provides a large improvement in one of these two criteria with little (or no) sacrifice in the other criterion. For this reason, it is of significant value to explore the trade-offs that arise between these two criteria. Accordingly, the key contribution of this paper is the formulation of a bicriterion optimization problem for anticlustering based on the diversity and dispersion criteria, along with heuristics to approximate the Pareto efficient set of partitions. A motivating example and computational study are provided within the framework of test assembly. 相似文献
77.
Antti Kangasrsi Jussi P. P. Jokinen Antti Oulasvirta Andrew Howes Samuel Kaski 《Cognitive Science》2019,43(6)
This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. 相似文献
78.
The level of moral development and moral intensity in cognitive psychology will not only affect the ethical behavior of accountants, but also have a direct impact on the quality and level of accounting work. Therefore, in this paper, the ethical behavior of accountants was analyzed from the perspective of cognitive psychology. Computer-aided data mining techniques were introduced, and government accounting risk assessment management of financial accountants was studied. In this paper, the principle of cognitive psychology to measure the ethical level of accountants was first described. The predicament of moral judgments was analyzed and an optimization plan to improve the ethical intention of accountants was proposed. Support Vector Machine classification technology in data mining was studied to explore how to conduct effective and reliable evaluation, so as to provide a scientific basis for decision-making in improving accounting management. After the simulation experiment, it is proved that continuously improving the ethical standards of accountants and strengthening the forecast of accounting risks can continue to optimize the accounting office management. 相似文献
79.
《Journal of Multi-Criteria Decision Analysis》2018,25(1-2):17-30
A distribution network operator can reconfigure the network topology by operating section switches to improve multiple objective functions, that is, the line losses and the lifespan of transformers. In the context of long term network management, the impact of future electricity deregulation should be taken into account. Because the related literature has not addressed the above problems, this paper presents a multicriteria optimization approach for efficient distribution network management under electricity market deregulation. The main contributions of this paper can be summarized as follows: (a) the time‐variable (time dependent) and nonconvex issues for the distribution network management are formulated such that the time series variations of the line losses and the transformers mechanical operations are modelled, (b) an electricity consumption model under the deregulation, in which consumers flexibly respond to electricity prices, is taken into account, and (c) from the practical viewpoint, the proposed evolutionary many‐objective optimization approach is applied to find approximated Pareto optimal solutions of the nonconvex large‐scale problem within practical operating time. It can be seen from the computational experiment that the proposed method enables the network operator to easily identify a better network topology, which adequately meets the multiple requirements of the operator. 相似文献
80.
《Journal of Multi-Criteria Decision Analysis》2018,25(3-4):88-100
Modern engineering requires finding an optimal trade‐off point among conflicting requirements that must be satisfied to reach the end user's satisfaction. One of the most challenging problems in multidisciplinary optimization is to find a fitness function that can best translate what the designer really aims to obtain. In this paper, a particle swarm optimization (PSO) algorithm is coupled to a fitness function based on the definition of a technique for order preference by similarity to ideal solution (TOPSIS) in order to obtain a new algorithm called PSOTOP. The fitness of a candidate solution is found by comparing its attributes to those of an “ideal best” and “ideal worst” solution, which is dynamically updated at each iteration of the algorithm. The advantage of this solution is that the fitness allows combining attributes of different magnitude and measurement units in an effective way; this approach can be applied to whatsoever optimization problem in engineering, economics, medicine, and statistics. This paper presents a case study dealing with the optimal design of an airfoil to be adopted on the wing of an unmanned air vehicle to support civil protection operations in order to show how this strategy can impact the design of a complex product. The main limitation of this approach relates to the fact that the user must possess a good knowledge of the specific problem to be solved in order to set proper ranges for design parameters and attributes' weight in the fitness evaluation. 相似文献