Trade‐off ranking method for multi‐criteria decision analysis

Multi‐criteria decision analysis presumes trade‐off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space. Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision‐maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision‐maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade‐off between different Pareto solutions. To be realized, the approach presumes the existence of a well‐distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm. The method is applied to a number of test cases and compared against two existing alternative approaches.     

Methodology to select solutions for multiobjective optimization problems: Weighted stress function method

The weighted stress function method is proposed here as a new way of identifying the best solution from a set of nondominated solutions according to the decision maker's preferences, expressed in terms of weights. The method was tested using several benchmark problems from the literature, and the results obtained were compared with those of other methods, namely, the reference point evolutionary multiobjective optimization (EMO), the weighted Tchebycheff metric, and a goal programming method. The weighted stress function method can be seen to exhibit a more direct correspondence between the weights set by the decision maker and the final solutions obtained than the other methods tested.     

对骨折治疗“优化选择”与“合理选择”的理解和思考

从合理化角度回顾了当前几种重要的骨折治疗理论体系的发展与演变,认为骨折治疗的最优原则是一种理想状态,现实中没有绝对最优只有相对合理。只有建立以患者为中心的思维体系,加强医疗逻辑思维能力,用多维思考的方式寻找矛盾的合理平衡点,始终坚守医学伦理道德底线,才能真正做到为每一位骨折患者进行最合理的治疗。     

神经导航在颅脑手术的最优化选择

评价神经导航系统在颅脑手术的应用价值.充分利用神经导航系统定位精确和实时导航功能,达到颅脑手术疗效的最优化.对神经导航在颅脑手术最优化方面的几点误区进行分析,对颅脑手术"适度医疗"的问题提出一些建议.     

Bayesian adaptive estimation: The next dimension

We propose a new psychometric model for two-dimensional stimuli, such as color differences, based on parameterizing the threshold of a one-dimensional psychometric function as an ellipse. The Ψ Bayesian adaptive estimation method applied to this model yields trials that vary in multiple stimulus dimensions simultaneously. Simulations indicate that this new procedure can be much more efficient than the more conventional procedure of estimating the psychometric function on one-dimensional lines independently, requiring only one-fourth or less the number of trials for equivalent performance in typical situations. In a real psychophysical experiment with a yes-no task, as few as 22 trials per estimated threshold ellipse were enough to consistently demonstrate certain color appearance phenomena. We discuss the practical implications of the multidimensional adaptation. In order to make the application of the model practical, we present two significantly faster algorithms for running the Ψ method: a discretized algorithm utilizing the Fast Fourier Transform for better scaling with the sampling rates and a Monte Carlo particle filter algorithm that should be able to scale into even more dimensions.     

An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity

Extreme learning machine (ELM) for random single-hidden-layer feedforward neural networks (RSLFN) has been widely applied in many fields in the past ten years because of its fast learning speed and good generalization performance. But because traditional ELM randomly selects the input weights and hidden biases, it typically requires high number of hidden neurons and thus decreases its convergence performance. It is necessary to select optimal input weights and hidden biases to improve the convergence performance of the traditional ELM. Generally, the single-hidden-layer feedforward neural networks (SLFN) with low input-to-output sensitivity will cause good robustness of the network, which may further lead into good generalization performance. Moreover, particle swarm optimization (PSO) has no complicated evolutionary operators and fewer parameters need to adjust, and is easy to implement. In this study, an improved ELM based on PSO and input-to-output sensitivity information is proposed to improve RSLFN’s convergence performance. In the improved ELM, PSO encoding the input to output sensitivity information of the SLFN is used to optimize the input weights and hidden biases. The improved ELM could obtain better generalization performance as well as improve the conditioning of the SLFN by decreasing the input-to-output sensitivity of the network. Finally, experiment results on the regression and classification problems verify the improved performance the proposed ELM.     

Inter- and intra-individual variability in the kinematics of the back squat

The purpose of this study was to explore the level of inter- and intra-individual variability in the kinematic profiles of the back squat movement among skilled weightlifters. Ten competitive weightlifters volunteered for participation in this study. Barbell velocity (V_Barbell) and angular velocity of the ankle (ω_Ankle), knee (ω_Knee) and hip joint (ω_Hip) were obtained by kinematic recording of six trials at 90% of 1RM in the back squat. Inter-individual variability was assessed by analysing inter-individual differences in the velocity curves through the statistical parametric mapping method. Intra-individual variability was assessed through a correlation analysis between the barbell velocity curves of each trial for each participant. Partial least squares regression analysis, was performed to relate changes in intra-individual variability to movement and anthropometric characteristics. Significant inter- and intra-individual differences were identified in V_Barbell, ω_Ankle, ω_Knee, and ω_Hip (p ≤ 0.05). Having a short trunk and thigh, and a long shin in combination with greater anterior-posterior displacement of the barbell and slower velocities during the acceleration phase increased intra-individual movement consistency over movement variability. The results of the present study clearly demonstrate that skilled weightlifters display both significant inter- and intra-individual variability in the successful execution of the back squat.     

Constructing robust crew schedules with bicriteria optimization

Optimization‐based computer systems are used by many airlines to solve crew planning problems by constructing minimal cost tours of duty. However, today airlines do not only require cost effective solutions, but are also very interested in robust solutions. A more robust solution is understood to be one where disruptions in the schedule (due to delays) are less likely to be propagated into the future, causing delays of subsequent flights. Current scheduling systems based solely on cost do not automatically provide robust solutions. These considerations lead to a multiobjective framework, as the maximization of robustness will be in conflict with the minimization of cost. For example crew changing aircraft within a duty period is discouraged if inadequate ground time is provided. We develop a bicriteria optimization framework to generate Pareto optimal schedules for the domestic airline. A Pareto optimal schedule is one which does not allow an improvement in cost and robustness at the same time. We developed a method to solve the bicriteria problem, implemented it and tested it with actual airline data. Our results show that considerable gain in robustness can be achieved with a small increase in cost. The additional cost is mainly due to an increase in overnights, which allows for a reduction of the number of aircraft changes. Copyright © 2003 John Wiley & Sons, Ltd.     

Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima

Cluster differences scaling is a method for partitioning a set of objects into classes and simultaneously finding a low-dimensional spatial representation ofK cluster points, to model a given square table of dissimilarities amongn stimuli or objects. The least squares loss function of cluster differences scaling, originally defined only on the residuals of pairs of objects that are allocated to different clusters, is extended with a loss component for pairs that are allocated to the same cluster. It is shown that this extension makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. A decomposition of the sum of squared dissimilarities into contributions from several sources of variation is described, including the appropriate degrees of freedom for each source. After developing a convergent algorithm for fitting the cluster differences model, it is argued that the individual objects and the cluster locations can be jointly displayed in a configuration obtained as a by-product of the optimization. Finally, the paper introduces a fuzzy version of the loss function, which can be used in a successive approximation strategy for avoiding local minima. A simulation study demonstrates that this strategy significantly outperforms two other well-known initialization strategies, and that it has a success rate of 92 out of 100 in attaining the global minimum.     

Human choice in "counterintuitive" situations: fixed- versus progressive-ratio schedules.

College undergraduates were given repeated opportunities to choose between a fixed-ratio and a progressive-ratio schedule of reinforcement. Completions of a progressive-ratio schedule produced points (exchangeable for money) and incremented that response requirement by 20 responses with each consecutive choice. In the reset condition, completion of a fixed ratio produced the same number of points and also reset the progressive ratio back to its initial value. In the no-reset condition, the progressive ratio continued to increase by increments of 20 throughout the session with each successive selection of this schedule, irrespective of fixed-ratio choices. Subjects' schedule choices were sensitive to parametric manipulations of the size of the fixed-ratio schedule and were consistent with predictions made on the basis of minimizing the number of responses emitted per point earned, which is a principle of most optimality theories. Also, the present results suggest that if data from human performances are to be compared with results for other species, humans should be exposed to schedules of reinforcement for long periods of time, as is commonly done with nonhuman subjects.