A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Sinha, Ankur; Malo, Pekka; Deb, Kalyanmoy

doi:10.1109/tevc.2017.2712906

Cited by 681 publications

(318 citation statements)

References 181 publications

(199 reference statements)

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“…Level 2 optimizes the hourly dispatch of BESS and DR programs in order to maximize the operational benefits of DSO. To solve the complex bilevel optimization problem, any evolutionary algorithm can be used . From literature survey, it is observed that GA is the most widely used technique for optimization problem of DG planning .…”

Section: Proposed Bilevel Optimization Methodologymentioning

confidence: 99%

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Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

Sharma

Niazi

Verma

et al. 2019

Int Trans Electr Energ Syst

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Summary The optimal integration of emerging distributed energy resources (DERs) such as energy storage systems and distributed generations (DGs) can improve the performance of distribution systems (DS) significantly. However, increased penetration of DGs may adversely affect the system performance under certain conditions in terms of increased energy losses, voltage violation, power mismatch, reverse power flow etc. The demand response (DR) is one of the operational technologies which may help overcome such issues related to increased DG penetration. In this paper, the role of DR is investigated to optimally accommodate multiple photovoltaics (PV) and battery energy storage systems (BESS) in DS. The problem is formulated as bilevel constrained optimization problem which has been solved using genetic algorithm (GA). The level 1 of optimization determines the optimal nodes and sizes of multiple BESS and PVs which are to be integrated in the system. Thereafter, dispatch of BESS in coordination with DR is optimized in the level 2 optimization. The simulation results highlight the effectiveness of DR in accommodating multiple PV and BESS in DS.

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Section: Proposed Bilevel Optimization Methodologymentioning

confidence: 99%

“…To solve the complex bilevel optimization problem, any evolutionary algorithm can be used. 36 From literature survey, it is observed that GA is the most widely used technique for optimization problem of DG planning. 37 Therefore, in this paper, GA has been used to solve the optimization problem at both levels.…”

Section: Proposed Bilevel Optimization Methodologymentioning

confidence: 99%

Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

Sharma

Niazi

Verma

et al. 2019

Int Trans Electr Energ Syst

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“…In general, the problem of designing CNN architectures for a target dataset D = {D trn , D vld , D tst } can be viewed as a bi-level optimization problem [7]. It can be mathematically formulated as,…”

Section: Introductionmentioning

confidence: 99%

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

et al. 2018

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While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.

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“…Mathematically, finding Nash equilibria of such Stackelberg games requires solving a bilevel optimization problem, which, in general cannot be undertaken analytically, [26], and numerical approaches are required. However, standard techniques are not able to deal with continuous and high dimensional decision spaces, as those appearing in AML applications.…”

Section: Introductionmentioning

confidence: 99%

Gradient Methods for Solving Stackelberg Games

Naveiro

Insua

2019

Algorithmic Decision Theory

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Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorithms and are usually continuous and high dimensional, e.g. choosing the weights of a neural network. As closed form solutions for Stackelberg games generally do not exist, it is mandatory to have efficient algorithms to search for numerical solutions. We study two different procedures for solving this type of games using gradient methods. We study time and space scalability of both approaches and discuss in which situation it is more appropriate to use each of them. Finally, we illustrate their use in an adversarial prediction problem.

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A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Cited by 681 publications

References 181 publications

Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

Bilevel optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Gradient Methods for Solving Stackelberg Games

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