Interval Robust Multi-objective Algorithm

Soares, Gustavo Luís; Parreiras, Roberta; Jaulin, Luc; Vasconcelos, J.A.; Maia, Carlos Andrey

doi:10.1016/j.na.2009.02.077

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…In our approach, J was calculated with all n possible combinations of management ({J k } k=1 n ), separately for the ETH (J E ) and SMHIRCA (J S ) climate scenarios to account for climate projection uncertainties and identify robust optimum solutions. A robust solution was defined here as the one with best performance for the worst case scenario (Soares et al 2009). This means in practice that, for every k, the minimum between J E and J S was selected to make a new series J * , which was maximized for every pixel.…”

Section: Spatial Optimization Routinementioning

confidence: 99%

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

et al. 2013

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In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a mesoscale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0-10 %, an increase in soil loss by 25-35 %, and an increase in N-leaching by 30-45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some prealpine grasslands to croplands. Keywords Agricultural land management Á Adaptation to climate change Á Crop modeling Á Regional optimization Á Multi-objective Electronic supplementary material The online version of this article (

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Section: Spatial Optimization Routinementioning

confidence: 99%

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

et al. 2013

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“…The Pareto set P of MOP (27) Similar in spirit are methods based on interval analysis (e.g., [47][48][49][50]). These interval analysis techniques ensure that the box collection is always a superset of the Pareto set, that is, that no sub-box that contains a part of P is rejected wrongly, leading to reliable numerical computations of the Pareto set.…”

Section: Set Oriented Approachesmentioning

confidence: 99%

Multiobjective Optimization for Space Mission Design Problems

Montao

Coello

Schtze

2014

Computational Intelligence in Aerospace Sciences

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“…For instance, Soares et al [25] built a guaranteed wrapper over the robust Pareto front using a deterministic algorithm based only on interval methods. Also, Soares et al [26] utilized an evolutionary approach to solve a constrained multi-objective electromagnetic design problem.…”

Section: Robust Multi-objective Problemmentioning

confidence: 99%

Interval Robust Multi-Objective Evolutionary Algorithm

Soares

Guimarães

Maia

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multiobjective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.

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Interval Robust Multi-objective Algorithm

Cited by 17 publications

References 21 publications

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

Multiobjective Optimization for Space Mission Design Problems

Interval Robust Multi-Objective Evolutionary Algorithm

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