Pareto Trade-off Curve Research Articles

Adverse impact reduction and job performance optimization via pareto-optimal weighting: A shrinkage formula and regularization technique using machine learning.

In personnel selection practice, one useful technique for reducing adverse impact and enhancing diversity is the Pareto-optimal weighting approach of De Corte et al. (2007). This approach produces a series of hiring solutions that characterize a diversity-job performance trade-off and can lead to more optimal selection outcomes (sometimes doubling the number of job offers for minority applicants without changing the job performance outcomes of personnel selection). Despite these advantages, recent research has identified a potential problem with the Pareto-weighting technique-Pareto solutions suffer from shrinkage upon cross-validation. To address the problem of shrinkage in the Pareto trade-off curve (i.e., diversity shrinkage and validity shrinkage), we offer two contributions. First, a shrinkage approximation formula is introduced (similar to a formula for adjusted R², but applicable to the entire Pareto trade-off curve). Second, we derive a novel technique for the regularization of Pareto-optimal predictor weights (borrowed from the field of machine learning), which is designed to produce predictor weights that are less vulnerable to shrinkage (similar to ridge regression and adapted from the elastic net technique). Both approaches-the proposed Pareto shrinkage formula and the proposed regularization technique-are then evaluated using Monte Carlo simulation. Recommendations are provided for approximating potential diversity-performance trade-off curves in personnel selection, while accounting for shrinkage. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Costing of strategies for long-term trunk main discolouration management

Material continually accumulates throughout drinking water distribution systems, as a result episodic maintenance is essential to mitigate uncontrolled mobilisation leading to water quality failings. Focussing on discolouration as the primary issue observed by consumers, this risk is of particular significance in trunk mains that can supply large downstream populations. Long-term total costs are for the first time investigated here by considering future operational and capital interventions to sustain a defined hydraulic capability that limits the discolouration response. To achieve this, accumulation and mobilisation profiles of pipe wall material is simulated using the open source Variable Condition Discolouration Model (VCDM) to develop Pareto trade-off curves between discolouration resilience and maintenance intervention frequency and magnitude. As the rate at which material accumulates is considered a function of water quality, operational savings that could accrue from reduced maintenance following capital investment, such as water treatment upgrades, are also investigated. With the complexity, size and ageing nature of water distribution system infrastructure, the ability to forecast network discolouration behaviour and hence costs is vital for long-term delivery of safe water at least cost to customers.

Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation

A challenging issue in optimal allocating water resources is uncertainty in parameters of a model. In this paper, a fuzzy multi-objective model was proposed to maximize the economic benefits of consumers and to optimize the allocation of surface and groundwater resources used for optimal cropping pattern. In the proposed model, three objective functions were optimizing farmer’s maximum net profit, groundwater stability and maximizing the reliability of water supply considering uncertainties in water resources and economic parameters in a basin. The optimal Pareto trade-off curves extracted using Non-dominated Sorting Genetic Algorithm- II. The best point on the Pareto trade-off curves was determined by using five decision-making approaches which combined by Breda aggregation method. Then, analyzing the credibility level of the optimization parameters and nonlinearity condition of objective functions revealed that by non-linearization of objective functions and increasing the fuzziness of the water demand and economic parameters, the model achieves more desirable values. Having been applied under uncertain conditions of objective functions and the input parameters, the results indicate an average increase of 17% and 54% in the allocation of agriculture and urban sectors, respectively. According to the annually optimal allocation results, the groundwater resources show higher sensitivity rather than surface water resources to the uncertainties in the parameters. Moreover, the optimal operation policies are more efficient than the deterministic model Consequently, the suggested model can facilitate optimizing water resources allocation policies, providing the optimal cropping pattern under uncertainty conditions, and can be used for the similar uncertain condition in other basins.

On the Regenerative Capabilities of Electrodynamic Dampers Using Bond Graphs and Model Predictive Control

There is a constant interest in the performance capabilities of active suspensions without the associated shortcomings of degraded fuel economy. To this effect, electrodynamic dampers are currently being researched as a means to approach the performance of a fully active suspension with minimal or no energy consumption. This paper investigates the regenerative capabilities of these dampers during fully active operation for a range of controller types—emphasizing road holding, ride, and energy regeneration. A model of an electrodynamic suspension is developed using bond graphs. Two model predictive controllers (MPCs) are constructed: standard and frequency-weighted MPCs. The resulting controlled system is subjected to International Organization for Standardization (ISO) roads A–D and the results are presented. For all of the standard MPC weightings, the suspension was able to recover more energy than is required to run the suspension actively. All of the results for optimal energy regeneration occurred on the standard Pareto tradeoff curve for ride comfort and road holding. Frequency weighting the controller increased suspension performance while also regenerating 3–12% more energy than the standard MPC.

Aeroelastic tailoring of a plate wing with functionally graded materials

A functionally graded material (FGM) provides a spatial blend of material properties throughout a structure. This paper studies the efficacy of FGM for the aeroelastic tailoring of a metallic cantilever plate-like wing, wherein a genetic algorithm provides Pareto trade-off curves between static and dynamic aeroelastic metrics. A key comparison is between the effectiveness of material grading, geometric grading (i.e. plate thickness variations), and using both simultaneously. The introduction of material grading does, in some cases, improve the aeroelastic performance. This improvement, and the physical mechanism upon which it is based, depends on numerous factors: the two sets of metallic material parameters used for grading; the sweep of the plate; the aspect ratio of the plate; and whether the material is graded continuously or discretely.

Aeroelastic Optimization of Flapping Wing Venation: A Cellular Division Approach

The aeroelastic response of a biologically inspired flappingmembrane wing to unsteady inertial and aerodynamic forces is strongly predicated upon the topological distribution of the underlying skeletal reinforcement (venation patterns). Proper exploitation of this relationship may enable the development of effective flapping wing micro air vehicles. The skeletal topology is parameterized into a developmental programwhich, when compiled and executed, evolves the wing topology in stages. A genetic algorithm can optimize the details of this program, rather than explicitly considering the topology itself, thus removing the link between design variables and topological geometry resolution. This cellular division tool is coupled to an unsteady aeroelastic representation of a flapping wing in forward flight in order to obtain the Pareto tradeoff curves between thrust generation, lift generation, and input power requirements. The topologies obtained along the front provide an understanding of the key relationships between skeletal topology, aeroelastic behavior, and performance metrics during flapping flight.

Optimal Structural Topology of a Platelike Wing for Subsonic Aeroelastic Stability

This work discusses the construction of a Pareto trade-off curve between the flight speed at which a plate-like wing encounters an aeroelastic stability, and the weight of that wing. The thickness of each finite element is utilized as a design variable in order to locate the optimal topological reinforcement as a function of the desired location along the Pareto front, as well as the planform of the wing. Three main challenges are addressed. First, the destabilizing flight speed must be located in an accurate and efficient manner. Secondly, the derivative of the flight speed with respect to a large number of thickness design variables must be computed analytically. Finally, the gradient-based optimization must contend with a discontinuous switch in the critical aeroelastic mode, slowing convergence.

Multi-Objective Topology Optimization of Wing Skeletons for Aeroelastic Membrane Structures

This work considers the multi-objective aeroelastic optimization of a membrane micro air vehicle wing through topology optimization. The low aspect ratio wing is discretized into panels: a two material formulation on the wetted surface is used, where each panel can be membrane (wing skin) or carbon fiber (laminate reinforcement). An analytical sensitivity analysis of the aeroelastic system is used for the gradient-based optimization of aerodynamic objective functions. An explicit penalty is added, as needed, to force the structure to a 0–1 distribution. Pareto trade-off curves are constructed by considering convex combinations of two disparate lift, drag, or pitching moment-based objective functions. The general relationship between spatial stiffness distribution (wing topology) and aerodynamic performance is discussed, followed by the Pareto optimality of the computed designs over a series of baseline wing structures. The work concludes with an experimental validation of the superiority of select optimal designs.

Fuzzy hierarchical decision support system for water distribution network optimization

In this article, fuzzy hierarchical multi-objective optimization has been applied for the design of water distribution networks, using genetic algorithms (GAs). The problem has been formally structured as a multi-level multi-criteria decision making (MCDM) process with two objectives: cost minimization and benefits maximization, resulting in a Pareto trade-off curve of non-dominated solutions. A number of criteria are introduced and individually assessed by fuzzy reasoning. The overall benefits function is a combination (aggregation) of criteria, according to their relative importance and their individual fuzzy assessment. It is obtained through an analytic hierarchy process, applied directly within the GA, using an original mathematical approach. The decision maker enters data and preferences using exclusively linguistic ‘engineer friendly’ definitions and parameters. In this way, the whole design algorithm moves away from strict numerical functions and acts as a decision support system for the water network design optimization. The model has been applied to ‘Anytown’, a well-known benchmark network.

Exploration of tradeoffs in intensity-modulated radiotherapy

The purpose of this study is to calculate Pareto surfaces in multi-criteria radiation treatment planning and to analyse the dependency of the Pareto surfaces on the objective functions used for the volumes of interest. We develop a linear approach that allows us to calculate truly Pareto optimal treatment plans, and we apply it to explore the tradeoff between tumour dose homogeneity and critical structure sparing. We show that for two phantom and two clinical cases, a smooth (as opposed to kinked) Pareto tradeoff curve exists. We find that in the paraspinal cases the Pareto surface is invariant to the response function used on the spinal cord: whether the mean cord dose or the maximum cord dose is used, the Pareto plan database is similar. This is not true for the lung studies, where the choice of objective function on the healthy lung tissue influences the resulting Pareto surface greatly. We conclude that in the special case when the tumour wraps around the organ at risk, e.g. prostate cases and paraspinal cases, the Pareto surface will be largely invariant to the objective function used to model the organ at risk.