scholarly journals Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

2012 ◽  
Vol 56 (1) ◽  
pp. 157-168 ◽  
Author(s):  
Sara Modarres Razavi ◽  
Di Yuan ◽  
Fredrik Gunnarsson ◽  
Johan Moe
Keyword(s):  
Pareto Optimization ◽  
Optimization Approach ◽  
Trade Off

Addressing the so-called validity–diversity trade-off: Exploring the practicalities and legal defensibility of Pareto-optimization for reducing adverse impact within personnel selection

2020 ◽  
Vol 13 (2) ◽  
pp. 246-271
Author(s):  
Deborah E. Rupp ◽  
Q. Chelsea Song ◽  
Nicole Strah
Keyword(s):  
Job Performance ◽  
Personnel Selection ◽  
Pareto Optimization ◽  
Adverse Impact ◽  
Optimization Approach ◽  
Trade Off ◽  
Selection Systems ◽  
Recent Method

AbstractIt is necessary for personnel selection systems to be effective, fair, and legally appropriate. Sometimes these goals are complementary, whereas other times they conflict (leading to the so-called “validity-diversity dilemma”). In this practice forum, we trace the history and legality of proposed approaches for simultaneously maximizing job performance and diversity through personnel selection, leading to a review of a more recent method, the Pareto-optimization approach. We first describe the method at various levels of complexity and provide guidance (with examples) for implementing the technique in practice. Then, we review the potential points at which the method might be challenged legally and present defenses against those challenges. Finally, we conclude with practical tips for implementing Pareto-optimization within personnel selection.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

scholarly journals A LEXICOGRAPHIC OPTIMIZATION APPROACH FOR BERTH SCHEDULE RECOVERY PROBLEM IN CONTAINER TERMINALS

Transport ◽  
2016 ◽  
Vol 31 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Qian Zhang ◽  
Qingcheng Zeng ◽  
Hualong Yang
Keyword(s):  
Container Terminals ◽  
Optimization Approach ◽  
Technical Problems ◽  
Trade Off ◽  
Practical Applications ◽  
Lexicographic Optimization ◽  
Multi Stage ◽  
Schedule Recovery ◽  
Equipment Failures ◽  
Recovery Problem

In container terminals, the planned berth schedules often have to be revised because of disruptions caused by severe weather, equipment failures, technical problems and other unforeseen events. In this paper, the problem of berth schedule recovery is addressed to reduce the influences caused by disruptions. A multi-objective, multi-stage model is developed considering the characteristics of different customers and the trade-off of all parties involved. An approach based on the lexicographic optimization is designed to solve the model. Numerical experiments are provided to illustrate the validity of the proposed Model A and algorithms. Results indicate that the designed Model A and algorithm can tackle the berth plan recovery problem efficiently because the beneficial trade-off among all parties involved are considered. In addition, it is more flexible and feasible with the aspect of practical applications considering that the objective order can be adjusted by decision makers.

Ranking strategies and threats: a cost-based pareto optimization approach

2009 ◽  
Vol 26 (1) ◽  
pp. 127-150
Author(s):  
Youngdae Kim ◽  
Gae-won You ◽  
Seung-won Hwang
Keyword(s):  
Pareto Optimization ◽  
Optimization Approach

scholarly journals Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach

2021 ◽  
Author(s):  
Maryam Parsa ◽  
Catherine Schuman ◽  
Nitin Rathi ◽  
Amir Ziabari ◽  
Derek Rose ◽  
...  
Keyword(s):  
Network Design ◽  
Pareto Optimization ◽  
Optimization Approach

scholarly journals Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Qian Zhang ◽  
Jinjin Ding ◽  
Weixiang Shen ◽  
Jinhui Ma ◽  
Guoli Li
Keyword(s):  
Particle Swarm Optimization ◽  
Greenhouse Gas Emission ◽  
Particle Swarm ◽  
Pareto Optimization ◽  
Environmental Cost ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Trade Off ◽  
Multiobjective Particle Swarm Optimization ◽  
Operation Cost

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

scholarly journals Efficient Robust Design Optimization of Optical Systems

2019 ◽  
Vol 215 ◽  
pp. 02001
Author(s):  
Stephanie Kunath
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Optical Design ◽  
Simulation Software ◽  
Robust Design Optimization ◽  
Optical Systems ◽  
Optical Simulation ◽  
Optimization Approach ◽  
Trade Off ◽  
Virtual Product Development

To accelerate the virtual product development of using optical simulation software, the Robust Design Optimization approach is very promising. Optical designs can be explored thoroughly by means of sensitivity analysis. This includes the identification of relevant input parameters and the modelling of inputs vs. outputs to understand their dependencies and interactions. Furthermore, the intelligent definition of objective functions for an efficient subsequent optimization is of high importance for multi-objective optimization tasks. To find the best trade-off between two or more merit functions, a Pareto optimization is the best choice. As a result, not only one design, but a front of best designs is obtained and the most appropriate design can be selected by the decision maker. Additionally, the best trade-off between output variation of the robustness (tolerance) and optimization targets can be found to secure the manufacturability of the optical design by several advanced approaches. The benefit of this Robust Design Optimization approach will be demonstrated.

scholarly journals Pareto Optimization for Subset Selection with Dynamic Cost Constraints

2019 ◽  
Vol 33 ◽  
pp. 2354-2361 ◽  
Author(s):  
Vahid Roostapour ◽  
Aneta Neumann ◽  
Frank Neumann ◽  
Tobias Friedrich
Keyword(s):  
Greedy Algorithms ◽  
Subset Selection ◽  
Pareto Optimization ◽  
Experimental Investigations ◽  
Optimization Approach ◽  
Submodular Optimization ◽  
Approximation Quality ◽  
Cost Constraints ◽  
Changes Over Time ◽  
Over Time

In this paper, we consider the subset selection problem for function f with constraint bound B which changes over time. We point out that adaptive variants of greedy approaches commonly used in the area of submodular optimization 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− α1f )-approximation, where αf is the sube modularity 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.

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