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 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.

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

A Multiobjective Resource Allocation Algorithm for Robust Project Scheduling

2016 ◽  
Vol 13 (10) ◽  
pp. 7701-7704
Author(s):  
Chao Chen ◽  
Changjun Fan ◽  
Xingxing Liang
Keyword(s):  
Resource Allocation ◽  
Multiobjective Optimization ◽  
Project Scheduling ◽  
Pareto Optimization ◽  
Benchmark Problems ◽  
Optimization Approach ◽  
Computational Results ◽  
Resource Allocation Problem ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

Resource allocation is an important procedure which involves allocating finite resources to the activities of a given baseline schedule. Based on the conception of Pareto Optimization, a multiobjective optimization approach for the resource allocation problem is proposed in this paper. The problem is first described. Then the detailed procedure of the proposed algorithm is given. Finally, an extensive computational results obtained on a set of benchmark problems are reported.

A novel multi-zone reactive power market settlement model: A pareto-optimization approach

Energy ◽  
2013 ◽  
Vol 51 ◽  
pp. 85-100 ◽  
Author(s):  
Amit Saraswat ◽  
Ashish Saini ◽  
Ajay Kumar Saxena
Keyword(s):  
Reactive Power ◽  
Power Market ◽  
Pareto Optimization ◽  
Optimization Approach ◽  
Reactive Power Market

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.

Sign in / Sign up

Export Citation Format

Share Document