stochastic model predictive control（SMPC） can be used in a broad variety of fields,such as diverse optimization *** issue which recently emerges is that some random variables contained in the objectivefunction or p...
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ISBN:
(纸本)9781509009107
stochastic model predictive control（SMPC） can be used in a broad variety of fields,such as diverse optimization *** issue which recently emerges is that some random variables contained in the objectivefunction or probabilistic constraints will have compound *** compound distributions are determined by some parameters which themselves are random and follow certain known *** paper will propose an effective and intelligible way to compute compound distributions with the use of a similar logic to the Falling Shadow *** to tackle the stochastic objective function,two typical formulations in risk studies,i.e.,the value-at-risk（VaR） and conditional value-at-risk（CVaR）,are introduced and formulated for SMPC.A simulation example regarding a coal trade decision-making process shows the efficacy of the proposed strategy.
This paper introduces a methodology for handling different types of uncertainties during robust optimization. In real-world industrial optimization problems, many types of uncertainties emerge, e.g., inaccurate settin...
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This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in det...
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This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in deterministic environments by incorporating a stochastic nondomination-based solution ranking procedure. In this study, concepts of stochastic dominance and significant dominance are introduced in order to better discriminate among competing solutions. The MOEA is applied to a number of published test problems to assess its robustness and to evaluate its performance relative to NSGA-II. Moreover, a new stopping criterion is proposed, which is based on the convergence velocity of any MOEA to the true Pareto optimal front, even if the exact location of the true front is unknown. This stopping criterion is especially useful in real-world problems, where finding an appropriate point to terminate the search is crucial.
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