Robust Optimisation
A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty
... of robust optimisation has been primarily aligned with mathematical programming ...convex optimisation problems, the problem incorporating uncertainty can be re-formulated to another tractable, ...
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Robust Optimisation for Hydroelectric System Operation under Uncertainty
... stochastic optimisation, the clustering algorithm is not employed for merging the forecasts obtained by sampling a given model ...our robust optimisation ...
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A robust optimisation approach using CVaR for unit commitment in a market with probabilistic offers
... Fig. 3 shows the normalised optimal balancing costs while varying the weighting ω on the CVaR risk measure. When the weighting is zero, the model does not take into account the costs introduced by the mis-estimation of ...
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Evidence-based robust optimisation of space systems with evidence network models
... system optimisation under uncertainty process is composed of the solution of a constrained bi-level min- max optimisation problem followed by the reconstruction of the Belief and Plausibility on the value ...
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Robust optimisation of low-thrust interplanetary transfers using evidence theory
... stops when the maximum number of iterations is reached. The iterative process allows to update the external surrogate models after each optimisation, so that accurate surrogate models can be locally obtained in ...
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Online Full Text
... the robust optimisation problem is formulated such that the springback as well as its variation is reduced subject to constraint on the sheet thinning ...the optimisation results is ...the ...
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Multi-objective robust trajectory optimisation under epistemic uncertainty and imprecision
... generate robust optimal trajectories for spacecraft equipped with low-thrust propulsion under the effect of epistemic ...are robust against the effect of ...the robust optimisation ...and ...
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Robust design of large-displacement compliant mechanisms
... cases, robust designs for small displacement compliant mechanisms are obtained in Wang et ...stochastic robust optimisation problem can be ...
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Genetic Algorithm Optimisation for Finance and Investments
... of robust optimi- sation of technical trading rules by using genetic algorithms with collective ...sharing. Robust optimisation is concerned with finding parameters values which are not necessarily ...
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Robust Rolling Horizon Optimisation Model for Offshore Wind Farm Installation Logistics
... scheduling, robust approaches are some of the methods to schedule operations under uncertain task ...In robust optimisation approaches, the range that the uncertain variable can take values should be ...
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An Evolutionary Approach to Active Robust Multiobjective Optimisation
... the optimisation, solution iden- tified as ‘optimal’ may poorly perform when implemented in real ...decades, robust optimisation (RO) has gained increasing popularity, with many studies aiming at ...
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Development of a decision support system for furrow and border irrigation
... and robust methodology for the unique identification of up to three infiltration/roughness ...Two optimisation- methods were investigated during this research with objective-functions based upon either a ...
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Development of a decision support system for furrow and border irrigation
... and robust methodology for the unique identification of up to three infiltration/roughness ...Two optimisation- methods were investigated during this research with objective-functions based upon either a ...
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Robust multi-objective optimisation of a descent guidance strategy for a TSTO spaceplane
... trix of the uncertain quantities at each altitude. Figure 4 shows the five generated temperature profiles, and their comparison with respect to the US76 model. A similar approach was followed for the density. As it can ...
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PID controller optimisation using genetic algorithms
... the optimisation process in general. However, the speed of the optimisation problem could be increased if the arbitrary value representing the chromosome was known to be near the optimal ...
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Robust and Optimal Methods for Geometric Sensor Data Alignment
... The Iterative Closest Point (ICP) algorithm [Besl and McKay, 1992; Chen and Medioni, 1992; Zhang, 1994] is the dominant solution for positional sensor data alignment with- out correspondences due to its conceptual ...
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Co evolving memetic algorithms: A learning approach to robust scalable optimisation
... the optimisation process, in the presence of regularities within the problem space which can be ex- ploited by a meta-learning process, paying particular atten- tion to the issue of ...
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Evolutionary methods for topology optimisation of continuum structures : static and dynamic problems
... obtain an optimal solution. Stmctural optimisation can be classed into three types:.. Oven/lew of Structural Optimisation. Size optimisation: the design variable can be the thickness of [r] ...
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Multi objective optimisation in air conditioning systems : comfort/discomfort definition by IF sets
... The benefit of the IF optimisation problems is twofold: they give richest apparatus for formulation of optimisation problems and, on the other hand, the solution of IF optimisation probl[r] ...
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OpenFlowChem – a platform for quick, robust and flexible automation and self optimisation of flow chemistry
... This operation mode may seem not advanced, however, it is the most suitable for routine screening under identical conditions, proof of principle tests or time on stream experiments.42,45[r] ...
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