Pareto Optimal Approach Research Articles

Author Correction: Optimizing fairness and accuracy: a pareto optimal approach for decision-making

Author Correction: Optimizing fairness and accuracy: a pareto optimal approach for decision-making

Optimizing fairness and accuracy: a Pareto optimal approach for decision-making

AbstractIn the era of data-driven decision-making, ensuring fairness and equality in machine learning models has become increasingly crucial. Multiple fairness definitions have been brought forward to evaluate and mitigate unintended fairness-related harms in real-world applications, with little research on addressing their interactions with each other. This paper explores the application of a Minimax Pareto-optimized solution to optimize individual and group fairness at individual and group levels on the Adult Census Income dataset as well as on the German Credit dataset. The objective of training a classification model with a multi-objective loss function is to achieve fair outcomes without compromising utility objectives. We investigate the interplay of different fairness definitions, including definitions of performance consistency and traditional group and individual fairness measures, amongst each other coupled with performance. The results presented in this paper highlight the feasibility of incorporating several fairness considerations into machine learning models, which can be applied to use cases with multiple sensitive features and attributes that characterize real-world applications. This research is a valuable step toward building responsible and transparent machine learning systems that can be incorporated into critical decision-making processes.

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

The utilization of flexible job shops is on the rise, driven by the decreasing life cycle of consumer products. In dynamic flexible job shops, the occurrence of disruptive events like machine breakdown, tool wear, and new job arrivals is common, emphasizing the critical need for efficient scheduling to achieve productivity and cost-effectiveness. Additionally with the growing concerns about energy consumption and global warming, the imperative to contemplate workshops with lower energy usage is becoming increasingly significant. Therefore, in this study a multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with insertion of new jobs for optimization objectives: makespan, total energy consumption and schedule instability is addressed. A novel multi-objective black widow spider algorithm (MOBWSA), inspired by the mating behavior of black widow spiders, is proposed. The MOBWSA ensures an optimal balance between minimizing makespan and energy consumption through its superior evolutionary processes of procreation, mutation and cannibalism. Furthermore, a strategic on/off strategy is implemented to enhance energy efficiency. The disruptions in schedule at rescheduling stage are addressed through the proposed sigmoidal weighted instability function. To handle the multi-objective nature of the problem this work incorporates Pareto-optimality approach in combination with novel hybrid crowding distance metric. The optimization is further aided by exploitation of search space through an ensemble of local search operators. Testing and evaluation of the MOBWSA with 30 benchmark problems is conducted, and its performance for multiple metrics is compared with recently published state-of-the-art algorithms. The results establish the effectiveness and efficiency of the proposed algorithm.

MOBCA: Multi-Objective Besiege and Conquer Algorithm.

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution.

Improving the Accuracy of Predictive 2D-LC Optimization Strategies: Incorporation of Simulated Elution Profiles to Account for Injection Band Broadening in Online Comprehensive Two-Dimensional Liquid Chromatography.

Method development in online comprehensive two-dimensional liquid chromatography (LC × LC) requires the selection of a large number of experimental parameters. The complexity of this process has led to several computer-based LC × LC optimization algorithms being developed to facilitate LC × LC method development. One particularly relevant challenge for predictive optimization software is to accurately model the effect of second dimension (2D) injection band broadening under sample solvent mismatch and/or sample volume overload conditions. We report a novel methodology that combines a chromatographic numerical simulation model capable of predicting elution profiles of analytes under conditions where peak distortion occurs with a predictive multiparameter Pareto optimization approach for online LC × LC. Preliminary method optimization is performed using a theoretical model to predict 2D injection profiles, and optimal experimental configurations obtained from the Pareto fronts are then subjected to further optimization using the simulation model. This approach drastically reduces the number of simulations and therefore the computational demand. We show that the optimal experimental conditions obtained in this manner are similar to those obtained using a complete optimization using only the simulation model. Online HILIC × RP-LC separation of phenolic compounds was used to compare experimental data to simulated two- and three-dimensional contour plots. The main advantage of the proposed approach is the ability to predict the formation of split or deformed peaks in the 2D, a significant benefit in online LC × LC method optimization, especially for separation combinations with mismatched mobile phases. A further benefit is that simulated elution profiles can be used for the visualization of predicted two-dimensional chromatograms for method selection.

Designing pareto-optimal selection systems for multiple minority subgroups and multiple criteria.

Currently used Pareto-optimal (PO) approaches for balancing diversity and validity goals in selection can deal only with one minority group and one criterion. These are key limitations because the workplace and society at large are getting increasingly diverse and because selection system designers often have interest in multiple criteria. Therefore, the article extends existing methods for designing PO selection systems to situations involving multiple criteria and multiple minority groups (i.e., multiobjective PO selection systems). We first present a hybrid multiobjective PO approach for computing selection systems that are PO with respect to (a) a set of quality objectives (i.e., criteria) and (b) a set of diversity objectives where each diversity objective relates to a different minority group. Next, we propose three two-dimensional subspace procedures that aid selection designers in choosing between the PO systems in case of a high number of quality and diversity objectives. We illustrate our novel multiobjective PO approaches via several example applications, thereby demonstrating that they are the first to reveal the complete gamut of eligible PO selection designs and to faithfully capture the Pareto trade-off front in case of more than two objectives. In addition, a small-scale cross-validation study confirms that the resulting PO selection designs retain an advantage over alternative designs when applied in new validation samples. Finally, the article provides a link to an executable code to perform the new multiobjective PO approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

AbstractParticle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high‐dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness‐constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness‐constrained model from magnetotelluric data utilizing multi‐objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

Optimal Sizing of Trailing Edge Flaps for Helicopter Vibration Reduction: A Response Surface Approach

On-blade active trailing edge flaps are considered for helicopter vibration reduction. A Pareto optimal multi-objective optimization approach is used to obtain the spanwise and chordwise dimensions of single and dual trailing- edge flaps. The objective of this study is to simultaneously achieve minimum hub vibration levels and minimize the flap actuation power requirement. Helicopter aeroelastic analysis is used in conjunction with an optimal controller to minimize hub vibration and flap power levels. It is found that nonlinear polynomial response surfaces based on L9 orthogonal array could adequately describe both the objectives. An intense sampling of the surrogate objective space is used to obtain optimal design points for the mutually conflicting objectives. A Pareto optimal design reduces vibration levels by about 70% and also reduces flap actuation power requirement by about 20%, in comparison to the initial design. The present study leads to an optimal design of trailing edge flaps which expend less power and can be further explored for self-powered control surfaces towards a self-powered smart blade design concept. The optimal designs are robust to uncertainty introduced by manufacturing.

Disturbance Rejection in MIMO Systems With Emotional-Learning-Based Controller: Application to Variable Rotor-Speed Helicopters

This manuscript proposes a Pareto-optimal approach toward designing an estimator for rejecting disturbances with an emotional-learning-based control (ELBC) administered MIMO system. Contrary to the conventional high-gain-based ELBC design, this approach employs a disturbance estimate augmented stimulus which is not prone to excitation of mechanical resonance. Furthermore, in contrast to the existing SISO-based methods of estimator design, this approach considers key attributes, such as disturbance rejection, noise sensitivity, control signal chattering, and loop interaction associated with multiple loops of an MIMO system in an integrated manner. The parameters of the estimator are realized by minimizing a performance index that is representative of the weighted sensitivity and complementary sensitivity functions of the MIMO system. The approach is illustrated with four popular disturbance estimators. Experimental results on a laboratory-scale helicopter under dynamic wind gusts demonstrate its efficacy with respect to nominal ELBC design.

A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

AbstractEvaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long‐established streamflow‐only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto‐based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade‐offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.

Ant Lion Optimized Lexicographic Model for Shortest Path Identification

Associated path detection is considered as the major concern of the traditional shortest path issue. The associated path is generally represented by the shortest distance among the source and destination. In the transportation network, distance or cost detection may identify this associated path. Specifically, it is very important to discover the shortest distance that has a minimum number of nodes, and it will give the most optimized result. In this paper, the Fuzzy based Pareto Optimal (FPO) approach is used to discover the shortest paths in a network graph. Initially, the FPO technique finds the shortest paths in a network by using set of rules. Then, the Lexicographical model uses a set of rules to rank the shortest distance based on minimum distance value. From the ranking results, the optimal shortest path is selected based on the proposed Ant Lion Optimization (ALO) algorithm. So, this paper achieves multi objectives like shortest path ranking and selection of the optimal shortest path. Time, distance or cost, convergence time, fitness function, and mean square error are the parameters used to relate the performance of the proposed technique with state-of-the-art techniques. Comparative results display the robustness and proficiency of the proposed system with several works.

Industrial Cyber–Physical System Defense Resource Allocation Using Distributed Anomaly Detection

An industrial cyber–physical system (ICPS) tightly integrating both physical processes and information and communication technologies (ICTs) leads to increasing cyberspace threats and attacks for the critical electrical infrastructure. With the limited defense resources availability, the efficient threat perception and mitigation of potential impacts of cyber attacks are essential to enhance the ICPS operational security. This article proposes an optimal defense resource allocation solution to prioritize the ICPS asset protection based on the distributed network traffic anomaly detection. The traffic anomalies and attack paths can be timely detected simultaneously over multiple security zones of the electrical infrastructure through local computing devices. The defense resource allocation is formulated as a multiobjective optimization (MOO) problem considering the tradeoff among the asset vulnerability, cost, and criticality, and solved by the Pareto optimal solution generation approach. The proposed solution is extensively evaluated using a realistic electrical CPS (ECPS) testbed for a range of cyber-attack scenarios. The numerical results confirm the effectiveness of the proposed distributed anomaly detection model and defense resource allocation strategy for varying defense resource availabilities.

A posteriori multiobjective techno-economic accommodation of DGs in distribution network using Pareto optimality and TOPSIS approach

A posteriori multiobjective techno-economic accommodation of DGs in distribution network using Pareto optimality and TOPSIS approach

Design Guidelines and Kinetic Performance Limits for Spatial Comprehensive Three-Dimensional Chromatography for the Analysis of Intact Proteins.

The design aspects of microfluidic chips for spatial three-dimensional chromatography featuring an interconnected channel network and targeting protein analysis are discussed, and the corresponding kinetic performance limits have been established using a Pareto-optimality approach. The pros and cons to integrate different separation mechanisms (IEF, CE, SEC, RPLC, HILIC, HIC, and IEX) are discussed considering development stages in the spatial domain (xLC) in the first and second dimension and time domain (tLC) for the third dimension. Based on Pareto-optimization, we discuss the considerations of the channel length, particle diameter, and the effect of number of second- and third-dimension channels on the resulting peak capacity of a spatial xIEF × xSEC × tRPLC device. Novel equations are proposed to determine the peak capacity in xSEC and to account for sample modulation affected by the number of second- and third-dimension channels. The corresponding Pareto fronts have been constructed demonstrating the resolving power, in terms of peak capacity and analysis time, considering current state-of-the-art prototyping methodologies. A microfluidic spatial prototype chip with an integrated channel layout (64 2D and 4096 3D channels) has been created, which has the potential to yield a peak capacity of 32,600 within only 44 min of the total analysis time, by implementing xIEF × xSEC × tRPLC separation stages.

Multi-objective optimization of pyroelectric thermal–electrical cycles

Pyroelectric thermal–electrical cycles enable a class of solid-state heat engines that convert waste heat to electrical energy. This article numerically investigates thermal-to-electrical energy conversion in a PbZr0.52Ti0.48O3 (PZT) pyroelectric layer near room temperature and optimizes operating parameters to maximize the electrical energy output. A general thermodynamic cycle is modeled after the prototypical pyroelectric Ericsson cycle—implemented based on the Ginzburg–Landau–Devonshire theory—with variable operating temperature range, and heating/cooling and charging/discharging time intervals. We used a Pareto optimization approach to simultaneously maximize electrical energy density and power density for different PZT sample and cycle parameters. The evaluated Pareto optimal fronts showcase the possibility of achieving multiple optimal solutions and highlight the trade-off between output energy density and power density in pyroelectric energy conversion. Specifically, we demonstrate that a 4× enhancement in power density is achievable with a less than 10% reduction in energy density for the same sample and operating conditions primarily by optimizing heat transfer. The multi-objective optimization approach and results presented in this study could provide a framework to facilitate the design and operation of pyroelectric cycles for waste heat energy harvesting systems.

A hybrid genetic algorithm based on a two-level hypervolume contribution measure selection strategy for bi-objective flexible job shop problem

This study addresses the bi-objective flexible job shop problem (BOFJSP) with respect to minimization of the maximum completion time (makespan) and total tardiness. This study aims to propose an algorithm called Bi-objective Hybrid Genetic Algorithm – hypervolume contribution measure (BOHGA-HCM) that integrates GA with a multi-search algorithm and uses hypervolume contribution measure (Δs) in its two-level selection strategy. The initial population is created by randomly assigning operations to the available machines via dispatching rules to find better areas in the search space and enhance diversity to avoid premature convergence. The algorithm handles the objective functions simultaneously with the Pareto Optimality approach. The effectiveness and performance of the proposed algorithm are benchmarked and compared with other algorithms by using well-known data sets presented in the literature.

A multi-objective network design model for road freight transportation using the eHighway system

New technological innovations of eco-friendly vehicles for freight transport combined with the usage of renewable energy sources showed significant results in mitigating transport-related carbon footprint. Therefore, in this paper, we present a multi-objective network design model considering a novel technology, the eHighway system, based on electrified roads to supply new Overhead Catenary (OC) hybrid trucks. This work investigates the opportunities of adopting eHighways and evaluates its environmental benefits considering limited budget resources for infrastructure electrification. We propose an optimization problem formulation including three objectives: the minimization of infrastructure and environmental costs, and the maximization of the total number of OC hybrid trucks served on electrified arcs. The Pareto optimization approach is considered for a comprehensive analysis of all possible solutions according to different criteria weights. The proposed model has been evaluated on a test network and the numerical results of Pareto optimization show the environmental improvement we can obtain by using the eHighway system up to about 99% according to the assumed available budget and assigned criteria weights. As a result, the model can be considered as a useful tool for decision-makers in the eHighway network design.

Method of Fuzzy Parametric Selection for Making a Reasonable Decision by an Intelligent Training Module

Selection of an optimal route within the intelligent approach provides for the possibility of applying soft models and computing in estimation of the trainee presence in the knowledge space. Among numerous ways of representation and processing of information of this type, the special place is held by those able to adapt to the maximal number of NO factors, characterizing the actual training situations, their measurable data and actual methods and ways of their processing that have ambiguities, uncertainties and incompleteness of the respective models and methods. In this paper, we suggest to extend the certain well-proven best practices in data analysis and transformation in the training environment information space to solving the actual training management problems. In addition, the paper demonstrates approaches to the use of Pareto-optimal approaches for fuzzy and underdetermined situations in actual training processes. Formally, this problem is solved with a fuzzy systemic graph. Variants of calculating procedures, allowing the use of the available apparatus of soft models and computing with the purpose to eliminate uncertainties when forming grounded decisions, are given. Methods and criteria of route options selection with regard of vaguely defined functional specification requirements have been developed pursuant to the study.

Multi-objective optimization of a reluctance actuator for precision motion applications

Recent research and assessments have shown that the reluctance actuator is a promising candidate to drive the next generation precision motion system of the lithography machine in the semiconductor industry. This paper proposes an multi-objective optimization algorithm for an optimal design of E-core reluctance actuator considering the requirements of the precision motion system. The objectives of optimization include maximizing the output magnetic force, minimizing the mass and the time constant of the actuator, and driving the actuator stiffness value to zero. First, the characteristics of the reluctance actuator are expressed in terms of the design variables of the reluctance actuator. After that, the optimization algorithm using the Grid search method and Pareto optimal approach is proposed to obtain the optimal design of the reluctance actuator. Finally, the optimal design of the reluctance actuator is implemented in a precision motion system example. The simulation results are obtained using Finite Element Analysis in COMSOL to check the saturation of magnetic flux in the reluctance actuator. The dynamic response of reluctance actuator motion system is obtained in the time domain for different inputs to verify the optimal design of the reluctance actuator.

Pareto optimization for subset selection with dynamic cost constraints

We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a ϕ=(αf/2)(1−1eαf)-approximation, where αf is the submodularity ratio of f, for each possible constraint bound b≤B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain ϕ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem. Our empirical analysis shows that, within the same number of evaluations, POMC is able to perform as good as NSGA-II under linear constraint, while EAMC performs significantly worse than all considered algorithms in most cases.