Het diversiteit-validiteitsdilemma in complexe selectiebeslissingen

2012 ◽  
Vol 25 (2) ◽  
Author(s):  
Celina Druart
Keyword(s):  
Optimization Algorithm ◽  
Adverse Impact ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Trade Offs ◽  
Selection Decisions ◽  
Selection Decision ◽  
Specific Position ◽  
Weighted Combination

The diversity-validity dilemma in complex selection decisions The diversity-validity dilemma in complex selection decisions The diversity-validity dilemma arises when valid selection predictors cause adverse impact. In this paper we discuss the diversity-validity dilemma within the complex selection context, which encompasses an applicant pool and several open positions. Some applicants apply for one of the available positions, while others apply for more than one or even all positions. Complex selection decisions select some applicants and assign them to a specific position. We present two methods to estimate the expected selection quality and adverse impact ratio (AIR) of such decisions, in case they are based on a weighted combination of several predictor scores. Additionally, these two methods are implemented in a multi-objective optimization algorithm to develop complex selection decisions that result in a Pareto-optimal trade-off between selection quality and AIR. Pareto-optimal trade-offs are trade-offs between the two outcomes that cannot be bettered simultaneously by any other (complex selection) decision.

scholarly journals Balancing ecosystem services with energy and food security – Assessing trade-offs from reservoir operation and irrigation investments in Kenya's Tana Basin

2014 ◽  
Vol 18 (8) ◽  
pp. 3259-3277 ◽  
Author(s):  
A. P. Hurford ◽  
J. J. Harou
Keyword(s):  
Ecosystem Services ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Management Strategies ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Management Problem ◽  
Economic Sectors ◽  
Trade Off ◽  
Trade Offs

Abstract. Competition for water between key economic sectors and the environment means agreeing allocations is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks firstly to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly, it seeks to show how trade-offs between achievable benefits shift with the implementation of proposed new rice, cotton and biofuel irrigation projects. To approximate the Pareto-optimal trade-offs we link a water resources management simulation model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume-dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for eight objectives covering the provision of water supply and irrigation, energy generation and maintenance of ecosystem services. Trade-off plots allow decision-makers to assess multi-reservoir rule-sets and irrigation investment options by visualising their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against the disturbance of ecosystems and local livelihoods that depend on them. Full implementation of the proposed schemes is shown to come at a high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of "water-energy-food nexus" resource security issues.

scholarly journals Comparative Study of Knee-Based Algorithms for Many-Objective Optimization Problems

2018 ◽  
Vol 12 (1) ◽  
pp. 7-16
Author(s):  
Saad M. Alzahrani ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Knee Region ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Small Set ◽  
Optimum Solution

Nowadays, most real-world optimization problems consist of many and often conflicting objectives to be optimized simultaneously. Although, many current Multi-Objective optimization algorithms can efficiently solve problems with 3 or less objectives, their performance deteriorates proportionally with the increasing of the objectives number. Furthermore, in many situations the decision maker (DM) is not interested in all trade-off solutions obtained but rather interested in a single optimum solution or a small set of those trade-offs. Therefore, determining an optimum solution or a small set of trade-off solutions is a difficult task. However, an interesting method for finding such solutions is identifying solutions in the Knee region. Solutions in the Knee region can be considered the best obtained solution in the obtained trade-off set especially if there is no preference or equally important objectives. In this paper, a pruning strategy was used to find solutions in the Knee region of Pareto optimal fronts for some benchmark problems obtained by NSGA-II, MOEA/D-DE and a promising new Multi-Objective optimization algorithm NSGA-III. Lastly, those knee solutions found were compared and evaluated using a generational distance performance metric, computation time and a statistical one-way ANOVA test.

scholarly journals EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested Trust-Region Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 3958
Author(s):  
Adrian Bekasiewicz ◽  
Slawomir Koziel ◽  
Piotr Plotka ◽  
Krzysztof Zwolski
Keyword(s):  
Optimal Design ◽  
Trust Region ◽  
Automatic Generation ◽  
Population Based ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Trust Region Algorithm ◽  
Multi Objective ◽  
Optimization Routine

Antenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional, experience-driven techniques are of use. In this work, a method for the automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with an adjustable number of parameters using a nested, trust region-based algorithm. The latter iteratively increases the dimensionality of the radiator in order to gradually improve its performance. The method has been used to generate a set of nine antenna designs, representing a trade-off between minimization of reflection within 3.1 GHz to 10.6 GHz and a reduction of size. The properties of the optimized designs vary along the Pareto set from −10 dB to −20 dB and from 230 mm2 to 757 mm2 for the first and second objectives, respectively. The presented design approach has been validated against a genuine, population-based optimization routine. Furthermore, the smallest Pareto-optimal design has been compared to the antennas from the literature.

scholarly journals Optimizing solar access and density in Tel Aviv: Benchmarking multi-objective optimization algorithms

2021 ◽  
Vol 2042 (1) ◽  
pp. 012066
Author(s):  
Thomas Wortmann ◽  
Jonathan Natanian
Keyword(s):  
Green Building ◽  
Urban Site ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Tel Aviv ◽  
Multi Objective ◽  
High Rise ◽  
Trade Offs ◽  
Solar Access

Abstract This paper explores the trade-off between redeveloping an urban site with higher density and maintaining solar access for the surrounding context in the hot and dry climate of Tel Aviv. Such trade-offs are important for future urban development in the Middle East, where densification is a demographic and environmental need. We explore this trade-off with multi-objective optimization (MOO). Specifically, we benchmark seven MOO algorithms on two test problems with different, parametric typologies: courtyard and high-rise. For both problems, we aim to maximize Floor Area Ratio and the simulation-based Context Exposure Index, a novel metric based on the Israeli green building code. The high-rise emerges as the better performing typology, and HypE, SPEA2, and RBFMOpt as the most efficient and robust MOO algorithms.

A Monotonic Archive for Pareto-Coevolution

2007 ◽  
Vol 15 (1) ◽  
pp. 61-93 ◽  
Author(s):  
Edwin D. de Jong
Keyword(s):  
Test Problem ◽  
Solution Concept ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Pareto Optimal Set ◽  
Number Of Layers ◽  
Open Question ◽  
Optimal Set ◽  
Selection Of

Coevolution has already produced promising results, but its dynamic evaluation can lead to a variety of problems that preventmost algorithms from progressing monotonically. An important open question therefore is how progress towards a chosen solution concept can be achieved. A general solution concept for coevolution is obtained by viewing opponents or tests as objectives. In this setup known as Pareto-coevolution, the desired solution is the Pareto-optimal set. We present an archive that guarantees monotonicity for this solution concept. The algorithm is called the Incremental Pareto-Coevolution Archive (IPCA), and is based on Evolutionary Multi-Objective Optimization (EMOO). By virtue of its monotonicity, IPCA avoids regress even when combined with a highly explorative generator. This capacity is demonstrated on a challenging test problem requiring both exploration and reliability. IPCA maintains a highly specific selection of tests, but the size of the test archive nonetheless grows unboundedly. We therefore furthermore investigate how archive sizes may be limited while still providing approximate reliability. The LAyered Pareto-Coevolution Archive (LAPCA) maintains a limited number of layers of candidate solutions and tests, and thereby permits a trade-off between archive size and reliability. The algorithm is compared in experiments, and found to be more efficient than IPCA. The work demonstrates how the approximation of amonotonic algorithm can lead to algorithms that are sufficiently reliable in practice while offering better efficiency.

Multi-Objective Optimization Design of Complex Mechanical and Electrical Products Based on Improved Evolutionary Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 1384-1388 ◽  
Author(s):  
Wei Wei ◽  
Li Hong Qiao
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Design ◽  
Design Space ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Trade Off ◽  
Pareto Optimal Set ◽  
Optimal Set

The design of complex mechanical and electrical products has to achieve various objectives and satisfy various constraints. In many cases, there are trade-off relationships between these objectives, and thus it is difficult to optimize these objectives simultaneously. This invokes the need of the multiobjective optimization to achieve these objectives collectively. In this paper, multiple objectives for complex mechanical and electrical products are optimized, simultaneously using an improved multiobjective evolutionary algorithm: ISPEA2. The results showed that ISPEA2 could generate uniformly a pareto optimal set in the design space and has better robustness and convergence than SPEA2 and NSGA-II.

scholarly journals Balancing ecosystem services with energy and food security – assessing trade-offs for reservoir operation and irrigation investment in Kenya's Tana basin

2014 ◽  
Vol 11 (1) ◽  
pp. 1343-1388 ◽  
Author(s):  
A. P. Hurford ◽  
J. J. Harou
Keyword(s):  
Ecosystem Services ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Management Strategies ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Management Problem ◽  
Economic Sectors ◽  
Trade Off ◽  
Trade Offs

Abstract. Competition for water between key economic sectors and the environment means agreeing on allocation is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly we seek to show how trade-offs between achievable benefits shift with the implementation of new proposed rice, cotton and biofuel irrigation projects. To identify the Pareto-optimal trade-offs we link a water resources management model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for objectives covering provision of water supply and irrigation, energy generation and maintenance of ecosystem services which underpin tourism and local livelihoods. Visual analytic plots allow decision makers to assess multi-reservoir rule-sets by understanding their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against disturbance of the flow regime which supports ecosystem services. Full implementation of the proposed schemes is shown to be Pareto-optimal, but at high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of water-energy-food "nexus" challenges.

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

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