Robustness Analysis in Multi-Objective Optimization Using a Degree of Robustness Concept

Barrico, Carlos; Antunes, Carlos Henggeler

doi:10.1109/cec.2006.1688537

Cited by 41 publications

(30 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The degree of robustness depends on the size of a δ-neighborhood of scenario s and the percentage of the h neighboring points whose objective function values for x are better than f s (x) or belong to the η-neighborhood of f s (x). Those h neighboring points are randomly generated around scenario s (see also [5], [9]- [11]). The degree of robustness of solution x is a value k, such that (see Fig.…”

Section: Perturbations Of the Objective Function Coefficientsmentioning

confidence: 99%

“…In this way, the approach used herein is based on the solution behavior in the neighborhood of the reference scenario. This concept of degree of robustness conveys more information to a DM than a simple robust/not robust classification and enables him/her to wield control on the level of robustness of solutions obtained through the setting of some parameters (see also [9]- [11]). The size of the reference scenario neighborhood can be specified, both regarding the objective function and constraint coefficients as well as the objective function space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Barrico

Antunes

Pires

2009

Evolutionary Computation in Combinatorial Optimization

View full text Add to dashboard Cite

Abstract.In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionary algorithm, which intervenes in the computation of the fitness value assigned to solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power flow, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.

show abstract

Section: Perturbations Of the Objective Function Coefficientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Barrico

Antunes

Pires

2009

Evolutionary Computation in Combinatorial Optimization

View full text Add to dashboard Cite

show abstract

“…2) The calculator of the degree of robustness Barrico proposed a new approach to robustness analysis in multi-objective optimization, involving the definition of robustness [6]. Though the utility of the arithmetic to fix the sample size of neighborhood kσ improved the calculation accuracy, it also leaded to high computational-complexity.…”

Section: ) Mean Effective Functionmentioning

confidence: 99%

Robust Multi-objective Optimization and Decision for Signal Timing at Single Intersection

Chen¹,

Jia²

2016

Proceedings of the 2016 International Conference on Automatic Control and Information Engineering

View full text Add to dashboard Cite

Abstract-For the purpose of improving the efficiency of traffic signal control with cycle perturbation for single intersections, a robust multi-objective optimization and decision method (RMODM) is proposed. Firstly, this paper presents a non-dominated sorting genetic algorithm II based on the degree of robustness (DR-NSGA-II) to obtain signal timing alternatives. Then, the Minimum deviation Analysis for Subjective and Objective Information (MDASOI) method is proposed to make signal timing decision. The experimental results indicate that the proposed method has preferable control effects.

show abstract

“…The majority of research in this area [2,6,7,12,13] defines robustness of solutions as insensitivity to small perturbations in the decision variables, and adds a robustness measure to the fitness assessment. Other definitions of robustness include: consistency of results between different runs [26], and reliability of results in uncertain environments, where the fitness function optimum is time varying [8].…”

Section: Related Workmentioning

confidence: 99%

“…In [2], the concept of degree of robustness was introduced to measure the robustness of solutions against small variations in decision variables. The aim of the study was to determine the effect of the value of a threshold p in the determination of the Pareto front of robust solutions.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective robustness for portfolio optimization in volatile environments

Hassan¹,

Clack²

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from highreturn/high-risk to low-return/low-risk, and an investor can choose her preferred point on the risk-return frontier. However, there are no guarantees that a low-risk solution will remain low-risk -if the environment changes, the relative positions of previously identified solutions may alter. A lowrisk solution may become high-risk and vice versa.The robustness of a Multiobjective Genetic Programming (MOGP) algorithm such as SPEA2 is vitally important in the context of the real-world problem of portfolio optimisation. We explore robustness in this context, providing new definitions and a statistical measure to quantify the robustness of solutions.A new robustness measure is incorporated into a MOGP fitness function to bias evolution towards more robust solutions. This new system ("R-SPEA2") is compared against the original SPEA2 and we present our results.

show abstract

Robustness Analysis in Multi-Objective Optimization Using a Degree of Robustness Concept

Cited by 41 publications

References 11 publications

Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Robust Multi-objective Optimization and Decision for Signal Timing at Single Intersection

Multiobjective robustness for portfolio optimization in volatile environments

Contact Info

Product

Resources

About