When solving complex constrained problems, how to efficiently utilize promising infeasible solutions is an essential issue because these promising infeasible solutions can significantly improve the diversity of algorithms. However, most existing cons...
In this work, we extend the classical framework of quantization for Borel probability measures defined on normed spaces
Recently, different metaheuristic techniques, their variants, and hybrid forms have been extensively used to solve economic load dispatch (ELD) problems with and without valve point loading (VPL) effects. Due to the randomization involved in these me...
In this paper, a symplectic algorithm is utilized to investigate constrained Hamiltonian systems. However, the symplectic method cannot be applied directly to the constrained Hamiltonian equations due to the non-canonicity. We firstly discuss the can...
Contingency Constrained Optimal Power Flow (CCOPF) differs from traditional Optimal Power Flow (OPF) because its generation dispatch is planned to work with state variables between constraint limits, considering a specific contingency. When it is not...
We have explored the use of constrained density functional theory (cDFT) for molecular junctions based on benzenediamine. By elongating the junction, we observe that the energy gap between the ionization potential and the electronic affinity increase...
The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with smal...
The traditional security-constrained optimal power flow (SCOPF) model under the classical N-1 criterion is implemented in the power industry to ensure the secure operation of a power system. However, with increasing uncertainties from renewable energ...
In the Loess Plateau of China, loess is widely distributed and forms a unique geomorphic feature of the world. Meanwhile, the Yellow River water and sediment regulation system is under construction. Nonetheless, the morphological characteristics of c...
Individuals encounter problems daily wherein varying numbers of constraints require delimitation of memory to target goal-satisfying information. Multiply-constrained problems, such as the compound remote associates, are commonly used to study this t...
Consider G to be a finite group and p to be a prime divisor of the order
We revisit the well-known Gilbert–Varshamov (GV) bound for constrained systems. In 1991, Kolesnik and Krachkovsky showed that the GV bound can be determined via the solution of an optimization problem. Later, in 1992, Marcus and Roth modified t...
Recently, research interest in the field of infrastructure attack and defense scenarios has increased. Numerous methods have been proposed for studying strategy interactions that combine complex network theory and game theory. However, the unavoidabl...
The Plug-and-Play framework has demonstrated that a denoiser can implicitly serve as the image prior for model-based methods for solving various inverse problems such as image restoration tasks. This characteristic enables the integration of the flex...
Emerging magnetic lead screws (MLSs) have been proven to be promising in sustainable wave energy conversion areas due to their high efficiency and power density. This study is aimed at developing a constrained maximum power point tracking (MPPT) stra...
This paper investigates robust constrained cooperative control for multiple trains, taking into account disturbances, velocity and control input constraints, and nonlinear operation resistances. A robust constrained cooperative control algorithm is e...
We consider autonomous conservative dynamical systems which are constrained with the condition that the total energy of the system has a specified value. We prove a theorem which provides the quadratic first integrals (QFIs), time-dependent and auton...
In this study, a new smoothing nonlinear penalty function for constrained optimization problems is presented. It is proved that the optimal solution of the smoothed penalty problem is an approximate optimal solution of the original problem. Based on...
In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) f...
Many strategies have been developed to modulate the biological or biotechnical properties of oligonucleotides by introducing new chemical functionalities or by enhancing their affinity and specificity while restricting their conformational space. Amo...
Constrained ordered weighted averaging (OWA) aggregation attempts to solve the OWA optimization problem subject to multiple constraints. The problem is nonlinear in nature due to the reordered variables of arguments in the objective function, and the...
Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy ra...
We introduce a novel entropy minimization approach for the solution of constrained linear regression problems. Rather than minimizing the quadratic error, our method minimizes the Fermi–Dirac entropy, with the problem data incorporated as const...
Sampling from constrained distributions has posed significant challenges in terms of algorithmic design and non-asymptotic analysis, which are frequently encountered in statistical and machine-learning models. In this study, we propose three sampling...
In this paper, we concentrate on an abstract iterative procedure for solving nonsmooth constrained generalized equations. This procedure employs both the property of weak point-based approximation and the approach of searching for a feasible inexact...
Attitude control subjected to pointing constraints is a requirement for most spacecraft missions carrying sensitive on-board equipment. Pointing constraints can be divided into two categories: exclusion zones that are defined for sensitive equipment...
Many modern real-valued optimization tasks use “black-box” (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global...
The present study addresses a critical issue within the realm of drones: the challenge of distributed constrained optimization. Our research delves into an optimization scenario where the decision variable is confined to a closed convex set. The prim...
Numerical analysis of a multibody mechanism moving in the air is a complicated problem in computational fluid dynamics (CFD). Analyzing the motion of a multibody mechanism in a commercial CFD software, i.e., ANSYS Fluent®, is a challenging issue....
The problem of testing the equi-correlation coefficient of a standard symmetric multivariate normal distribution is considered. Constrained Bayesian and classical Bayes methods, using the maximum likelihood estimation and Stein’s approach, are...
The k-means problem has been paid much attention for many applications. In this paper, we define the uncertain constrained k-means problem and propose a
Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of da...
The sheer size and vast chemical space (i.e., diverse repertoire and spatial distribution of functional groups) underlie peptides’ ability to engage in specific interactions with targets of various structures. However, the inherent flexibility of the...
Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering t...
Constrained-surface-based stereolithography has recently attracted much attention from both academic and industrial communities. Despite numerous experimental, numerical and theoretical efforts, the fundamental need to reduce the separation force bet...
Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respon...
This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a sin...
The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot...
Resource-constrained wireless networks are emerging networks such as Radio Frequency Identification (RFID) and Wireless Body Area Networks (WBAN) that might have restrictions on the available resources and the computations that can be performed. Thes...
The energy-absorbing capacity and friction phenomena of different closed-cell aluminium foam-filled Al tube types are investigated through experimental compression tests. Concerning the kind of investigation, free, radial-constrained and friction tes...
The evolution form of the flow field generated via the impact of a muzzle jet on a constrained moving body changes from the state of fully free-spatial development to that of constrained development, and it involves the problem of interference, owing...
The fundamental problem with causal inference involves discovering causal relations between variables used to describe observational data. We address this problem within the formalism of information field theory (IFT). Specifically, we focus on the p...
The purpose of this paper is to present a novel optimization framework that enhances Wasserstein Distributionally Robust Optimization (WDRO) for chance-constrained facility location problems under demand uncertainty. Traditional methods often rely on...
Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspire...
Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the...
The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable...
Based on the effect of damped shear deformation on energy dissipation, a new constrained damping base for a polymer injection platform deck is proposed to reduce the excessive vibrations caused when multiple plunger pumps are jointly operated. A mode...
Extensive research on resource-constrained innovation has been conducted by scholars and practitioners in recent years. An interesting research avenue is how firms explore the process of the new product development (NPD) and the ideas generation to f...
The production of primary aluminum is an energy-intensive industry which produces large amounts of direct and indirect greenhouse gas emissions, especially from electricity consumption. Carbon Emissions Constrained Energy Planning proved to be an eff...
The constrained disorder principle (CDP) defines complex biological systems based on inherent variability. Allostasis refers to the physiological processes that help maintain stability in response to changing environmental demands. Allostatic load de...
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