Results 41 to 50 of about 17,775 (173)
, 2017 This article considers nonconvex global optimization problems subject to uncertainties described by continuous random variables. Such problems arise in chemical process design, renewable energy systems, stochastic model predictive control, etc.
Scott, Joseph Kirk, Shao, Yuanxun
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The Canadian Journal of Chemical Engineering, EarlyView.Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos +3 more
wiley +1 more source
Energy Science &Engineering, EarlyView.The proposed hybrid osprey‐salp swarm optimization algorithm addresses optimal power flow (OPF) problems in smart grids incorporating solar, hydro, and thermal generators. The algorithm is validated on Institute of Electrical and Electronics Engineers 30‐, 57‐, and 118‐bus test systems across five single and multiobjective OPF scenarios.
Mujtaba Ali +5 more
wiley +1 more source
Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints [PDF]
, 2011 This paper investigates the relation between sequential convex programming (SCP) as, e.g., defined in [24] and DC (difference of two convex functions) programming.
Diehl, Moritz, Quoc, Tran Dinh
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Energy Science &Engineering, EarlyView.To enhance the power restoration speed of networked microgrids (NMGs) after extreme natural disasters and reduce the power outage of the system, this paper proposes a rapid post‐disaster restoration method for NMGs based co‐optimization of fault repair and load restoration.
Yunfan Zhang +3 more
wiley +1 more source
, 2018 In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed
Chatzinotas, Symeon +3 more
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Safe Stabilization Using Non‐Smooth Control Lyapunov Barrier Function
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper addresses the challenge of safe stabilization, ensuring the system state reaches the origin while avoiding unsafe state regions. Existing approaches that rely on smooth Lyapunov barrier functions often fail to guarantee a feasible controller. To overcome this limitation, we introduce the non‐smooth control Lyapunov barrier function (
Jianglin Lan +3 more
wiley +1 more source
BMC Bioinformatics, 2009 Background Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering.
Sorribas Albert +1 more
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Catalyst Acceleration for Gradient-Based Non-Convex Optimization [PDF]
, 2017 We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions.
Drusvyatskiy, Dmitriy +4 more
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A Quantised Push‐Sum Distributed Adaptive Momentum Algorithm for Optimisation Over Directed Networks
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT In this paper, we investigate a distributed constrained optimisation problem over directed networks. The agents in the networks conduct local computations and communications, endeavouring to collaboratively minimise the aggregation of all locally known convex cost functions subject to a global constraint set.
Qingguo Lü +6 more
wiley +1 more source