Proposed Optimization Problem Research Articles

Some metaheuristic algorithms for solving multiple cross-functional team selection problems.

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

Optimization algorithm for feedback and feedforward policies towards robot control robust to sensing failures

Background and problem statementModel-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires that a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed.ContributionIn this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the proposed optimization problem considers minimization/maximization of divergences between trajectories, one is predicted by the composed policy and a stochastic dynamics model, and others are inferred as optimal/non-optimal ones. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion.

Color image restoration based on saturation-value total variation plus L1 fidelity* *Wang is supported by Natural Science Foundation of Shanghai (18ZR1441800, 22ZR1465300) and the Fundamental Research Funds for the Central Universities.

Color image restoration is one of the most important tasks in image processing. In this paper, we propose and develop a novel saturation-value total variation (SVTV) plus L1 fidelity (SVTV-L1) model for color image restoration. SVTV is incorporated in the proposed model for regularizing the objective color image, meanwhile, L1 norm is considered for data fitting term in the proposed energy functional. Theoretically, we investigate the properties of the objective functional and the objective model. Note that the proposed energy functional including L1 norm is nonsmooth. We develop a smooth approximation approach to study the existence and properties of the solution of the proposed optimization problem. Numerically, Poisson noise and mixed noise have been tested and studied in the experimental results. We formulate an effective and efficient algorithm to solve the proposed minimization problem based on the framework of alternating direction method of multipliers. Numerical examples are presented to demonstrate that the performance of the proposed SVTV-L1 model is better than that of other testing methods in terms of visual quality, peak signal-to-noise ratio, and structure similarity.

Stability Improvement of a Transmission Grid With High Share of Renewable Energy Using TSCOPF and Inertia Emulation

This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calculates the optimal operating point of the system, accommodating high shares of non-synchronous renewable generation while ensuring transient stability in the event of critical incidents. Synthetic inertia controllers are used to improve the dynamic stability of the system in cases of very high share of renewable generation. The proposed tool is tested in the North-West Spanish system, a network with a high penetration of wind energy that causes a reduction in the total system inertia. The results of the study show that 1) synthetic inertia in renewable power plants can diminish electromechanical oscillations after a severe contingency, reducing the cost of ensuring transient stability; 2) using synthetic inertia the system becomes more stable when conventional generation is decommissioned following de-carbonization and renewable promotion policies; and 3) the proposed model can be used to calculate the parameters of the synthetic inertia control.

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software’s capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model’s characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models’ characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library.

Resource Allocation Optimization Based on Energy Efficiency in Green Cloud Radio Access Network

Compared with traditional cellular networks, cloud wireless access network (C-RAN) can save more energy consumption because it centrally processes the signals in the baseband unit (BBU) pool served by the cloud platform. Aiming at the limited fronthaul capacity of the fronthaul link caused by massive data and the huge energy consumption caused by the explosive growth of mobile traffic, an energy-saving resource management scheme that is powered by a mixture of on-grid and green energy is proposed in this paper. Specifically, a new network energy efficiency (EE) model is constructed. The RRH with hybrid energy to supply power of the model in response to green communication and the model can also realize two functions: information decoding and wireless charging at the same time. The total power consumption including remote radio head (RRH), fronthaul link, BBU pool, and wireless charging equipment of user equipment is considered. Based on network energy efficiency model, in addition to considering the user’s quality of service (QoS), data rate requirements, transmission power of RRHs, and maximum battery capacity constraints, the constraints of fronthaul link capacity constraints also are considered in this paper. In order to solve the proposed optimization problem, a joint strategy of power allocation, resource block (RB) allocation, and power allocation ratio adjustment is proposed in this paper. However, the objective function of energy efficiency optimization problem is nonconvex and cannot be solved directly. Therefore, an equivalent convex feasibility problem is reconstructed, and the optimal solution is obtained by Lagrange dual decomposition method. Simulation results show that the proposed resource allocation scheme can provide higher energy efficiency for the network.

Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm

The increasing unmanned aircraft system (UAS) applications in urban environments pose challenges for safe and efficient low altitude air traffic management. As an essential enabler to meet these challenges, pre-flight 4D routes optimization is required to conduct conflict detection and resolution (CD&R) and to generate conflict-free flight routes before departure. Existing studies on strategic deconfliction cover several types of strategies such as scheduling or rerouting. However, a single type of strategy used to solve different types of conflicts may lead to an unsafe and inefficient way of conflict resolution. This paper proposes an adaptive decision-making framework to optimize the resolution strategies used for different types of conflicts with explainable mechanisms. The proposed framework is formulated as a double-layer optimization problem with the considerations of scheduling, speed adjustment, and rerouting strategies for conflict resolution. The first layer of the framework is established as a probabilistic selection model to make decisions on which strategy should be selected for what type of conflict. The second layer is developed as a mixed-integer nonlinear programming (MINLP) model to optimize the decision variables of the strategies selected by the first layer. To solve the proposed double-layer optimization problem, we introduce and improve a novel meta-heuristic stochastic fractal search (SFS) algorithm with two major improvements of a penalty-guided fitness function and an exploitation-exploration balancing scheme. Simulation results demonstrate that the proposed adaptive conflict resolution framework successfully optimizes the strategies used for each type of flight conflict, which subsequently optimizes the 4D routes with significant reductions in total operational cost, number of flight conflicts, and flight delays. The improved stochastic fractal search (ISFS) algorithm is also proved effective and reliable in solving the proposed optimization problem in different traffic density scenarios.

Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

AbstractThe proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi‐level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)‐based forecasting method. To enhance the flexibility of the system, a price‐based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.

Real-Time Power Management Including an Optimization Problem for PV-Powered Electric Vehicle Charging Stations

Electric vehicles (EVs) are expanding quickly and widely, and, therefore, EVs can participate in reducing direct greenhouse gas emissions. The intelligent infrastructure for recharging EVs, which is microgrid-based, includes photovoltaic (PV) sources, stationary storage, and a grid connection as power sources. In this article, the energy cost optimization problem is studied, taking into account the intermittent arrival and departure of EVs. A mixed-integer linear programming is formulated as an optimization problem in a real-time operation to minimize the total energy cost, taking into consideration the physical limitations of the system. The interaction with the human-machine interface provides EV data in real-time operation, and the prediction only communicates the PV prediction profile provided by the national meteorological institute in France. The optimization is executed at each EV arrival, with the actualized data in the DC microgrid. Simulation and real-time experimental results of different meteorological conditions show that the EV user demands are satisfied, proving the feasibility of the proposed optimization problem for real-time power management.

Optimal 3D Placement of UAV-BS for Maximum Coverage Subject to User Priorities and Distributions

The usage of unmanned aerial vehicle (UAV) as a base station is in the spotlight to overcome the severe attenuation characteristics of short-wavelength radio in high-speed wireless networks. In this paper, we propose an optimal UAV deployment algorithm, considering the priority of ground nodes (GNs) in different wireless communication environments. Specifically, the optimal position of a UAV is determined so that as many high-priority GNs can be served rather than covering as many GNs as possible. The proposed optimization problem deals with two groups of GNs with different priorities and finds the optimal position of the UAV by solving the mixed-integer second-order cone problem (MISOCP). To verify the effectiveness of the proposed optimal UAV deployment algorithm, we conduct various evaluating scenarios with different urban environments and GN spatial distributions. We also compare the performance of the proposed algorithm with the conventional one. Simulation results show that the proposed scheme achieves superior coverage efficiency, throughput, and delay performance compared to the conventional algorithm, even when the environment and the spatial distribution of GNs are changed.

Optimal CVSR allocation to ensure network health in static load shedding– a Bi-level optimization approach

In this paper, a new methodology is proposed for achieving a general load shedding scheme to prevent power system cascading line outages with the help of continuously variable series reactors (CVSRs). In the proposed methodology, first, critical lines are determined using a new index. Critical lines here are those lines that, if tripped out, make the post-disturbance system operate closer to instability limits. An innovative optimization problem is then solved to find a general load shedding scheme to cover the single outage of all critical lines so that three objectives, i.e. maximizing voltage stability of the system and minimizing voltage deviations as well as overloading of lines, are obtained. In addition, since CVSR as a recently introduced compensating device has the capability of increasing the capacity of lines, which is so essential for load shedding strategies, a CVSR allocation problem is also added to the proposed optimization problem. However, as the problem of maximizing voltage stability has to be solved separately from the main optimization problem, a bi-level optimization approach has been introduced. Therefore, there will be two levels of optimization each one has its own solver and variables. The effectiveness of the proposed load shedding optimization approach has been demonstrated using 30-bus test system as a test system and 200-bus Illinois system as a real system.

Optimization of fast gray matter acquisition T1 inversion recovery (FGATIR) on 7T MRI for deep brain stimulation targeting

Deep brain stimulation (DBS) is an increasingly utilized treatment for multiple neurological disorders. Continued improvements in DBS outcome are, in part, related to increasing ability to directly visualize stimulation targets by MRI. However, it is challenging to image DBS targets with conventional MRI techniques due to limited contrast. Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) is a commonly used MRI sequence that improves visualization of several key DBS targets by suppressing white matter (WM) signal to better reveal deep-brain gray matter (GM) structures. Due to increased signal level at high field strength, application of FGATIR on 7T MRI may allow higher spatial resolution and better DBS targeting accuracy. However, successful utilization of FGATIR requires meticulous sequence optimization involving multiple parameters to maximize GM signal while suppressing WM. This is further complicated by the transmit RF field (B1+) inhomogeneity on 7T, which can cause severe contrast degradation. In this work, we introduce a systematic approach to optimize FGATIR and to improve visualization of thalamic DBS targets on 7T. FGATIR optimization is cast into a constrained optimization problem whose objective function and constraints are designed to maximize the GM-WM contrast-to-noise ratio (CNR) while accounting for B1+ inhomogeneity. This approach allows a systematic search for optimal parameters across the multi-dimensional parametric space while limiting the negative effect of B1+ variation. Bloch equation simulations were performed to solve the proposed optimization problem and to compare the sequence derived from this method against the sequence optimized without considering B1+ inhomogeneity. The results showed that this approach can improve GM-WM CNR in the presence of B1+ inhomogeneity, especially in some high relative B1+ areas where several key thalamic DBS targets are located. Additionally, in vivo images were acquired on a clinical 7T MRI to further validate this approach. Severe contrast degradation in the thalamus was observed when B1+ effect was not considered in sequence optimization, while the proposed approach yielded improved image contrast in the thalamus with key DBS targets well-defined. These results demonstrated that the proposed method allowed optimization of FGATIR on 7T to better visualize thalamic DBS targets, which may lead to improved DBS targeting accuracy as well as treatment outcome.

Global dynamic optimization with Hammerstein–Wiener models embedded

Hammerstein–Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis of input–output data without requiring identification of a full nonlinear process model. Optimization problems with Hammerstein–Wiener models embedded are nonconvex, and thus local optimization methods may obtain suboptimal solutions. In this work, we develop a deterministic global optimization strategy that exploits the specific structure of Hammerstein–Wiener models to extend existing theory on global optimization of systems with linear dynamics. At first, we discuss alternative formulations of the dynamic optimization problem with Hammerstein–Wiener models embedded, demonstrating that careful selection of the optimization variables of the problem can offer significant numerical advantages to the solution approach. Then, we develop convex relaxations for the proposed optimization problem and discuss implementation aspects to obtain the global solution focusing on a control parametrization technique. Finally, we apply our optimization strategy to case studies comprising both offline and online dynamic optimization problems. The results confirm an improved computational performance of the proposed solution approach over alternative options not exploiting the linear dynamics for all considered examples. They also underline the tractability of deterministic global dynamic optimization when using few control intervals in online applications like nonlinear model predictive control.

Co-residence based data theft game in cloud system with virtual machine replication and cancellation

In cloud computing, the task replication with cancellation (TRC) approach aims to reduce the expected task completion time while mitigating additional load and user expenses. However, its effectiveness may be hindered by co-residence attacks, where attackers may access users’ data through co-residing their virtual machines (VMs) with users’ VMs on the same server. Particularly, creating more task replicas has two-fold effects: a task has larger chances to complete in a shorter time but becomes more vulnerable to co-residence attacks (incurring higher data theft probability). The objective of this paper is to formulate and solve an optimization problem (a minmax game problem) that finds the optimal number of task replicas balancing the two conflicting effects. Resulting solutions to the proposed optimization problem can maximize the user's utility while considering the strategic attack behavior of maximizing the attacker's utility. The solution methodology encompasses a new probabilistic model of evaluating the user's and attacker's utilities. As demonstrated through examples, there exist complicated interactions of different model parameters impacting the utilities of users and attackers and thus the optimization solutions, justifying necessity and significance of the suggested model and methodology.

Weak multi-label learning with missing labels via instance granular discrimination

In multi-label learning, each training instance is associated with multiple class labels. It is typical in reality that relevant labels are partially missing and only a part of labels are valid, resulting in the problem of weak multi-label learning with missing labels. It is still an evident challenge to estimate the ground-truth label matrix and to generate a prediction function, especially on the multi-label data with a large number of missing labels. In this paper, we propose a multi-label learning framework within which feature structure and label manifold are both utilized to recover the incomplete label matrix and to train the classification model simultaneously. To mitigate the imbalanced risks brought by the sparse label issue, a self-adaptive penalty factor is imposed on the deviated predictions of different labels. Moreover, instance granular discrimination is incorporated in the framework to characterize the approximate distribution structure of data. Matrix vectorization, cave-convex programming (CCCP), and block coordinate descent techniques are employed to solve the proposed optimization problem. Extensive experiments illustrate the superiority of the proposed method against some well-established methods.

Consensus‐based robust least‐squares filter for multi‐sensor systems

SummaryIn this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this approach, a min‐max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least‐squares problem. Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus‐based robust least‐squares filter, it has been applied to an uncertain multi‐sensor system with 100 nodes and its results have been compared with existing consensus filters.

A novel nested loop optimization problem based on deep neural networks and feasible operation regions definition for simultaneous material screening and process optimization

The present work proposes a novel strategy for simultaneous material screening and process optimization. This strategy is based on the capacities of deep neural networks to extract knowledge of a database. It makes use of a nested optimization loop which is designed to couple the process and material points of view simultaneously. The optimization problem results are analyzed by a Fisher–Snedecor test, designed to assess the optimal points uncertainties, building the process’s feasible operating regions. This methodology describes the processes’ possible operating points that lead to optimal conditions, considering the material type as a decision variable. The methodology shows that this complex problem can be better understood when the uncertainties are taken into consideration. On the other hand, the proposed optimization problem can provide a way to address the issues related to optimizing adsorption processes considering the material screening.

Transient stability constrained optimal power flow applied to distribution systems with synchronous generators

This paper proposes a Transient Stability Constrained Optimal Power Flow (TSC-OPF) formulation in order to calculate the optimum operating point of synchronous generators in distribution networks, in terms of dispatch of their active and reactive powers. However, the problem is complex due to the high number of constraints and variables, as well as, the presence of nonlinear constraints. To circumvent these adversities, an optimization problem is proposed in this paper with the application of a set of mathematical approximations in the constraints of the active and reactive power balance equations. In addition, an approximation is explored in the swing equation of synchronous generators belonging to a group of coherent machines, particularly regarding the active power injection from the generators. The proposed TSC-OPF is tested in a 31-bus radial distribution system with two and four generators. The results show that the running time to solve the proposed optimization problem with approximations becomes smaller when compared to the time to solve it without approximations.

Adaptive Random Spatial based Channel Estimation (ARSCE) for Millimeter Wave MIMO System

Millimeter-wave and mMIMO communications are the most essential success systems for next-generation wireless sensor networks to have enormous amounts of accessible throughput and spectrum. Through installing huge antenna arrays at the base station and performing coherent transceiver processing, mMIMO is a potential technology for enhancing the bandwidth efficiency of wireless sensor networks. The use of mmWave frequencies for mMIMO systems solves the problem of high path-loss through offering greater antenna gains. In this work, we provide a design with a random spatial sample structure that incorporates a totally random step before the analogue is received. It contains a totally random step before the analogue received signals are sent into the digital component of the HBF receiver. Adaptive random spatial based channel estimation (ARSCE) is proposed for channel session measurement collection, and an analogue combiner with valves has been used to estimate the signals at each receiving antenna. The proposed optimization problem formulation attempts to discover the orientations and gains of wideband channel routes. In addition, our proposed model has compared to various state-of-art techniques while considering error minimization.

Feedback Controller Optimization for Active Noise Control Headphones Considering Frequency Response Mismatch between Microphone and Human Ear

This paper presents an investigation on the feedback controller design for active noise control headphones under the condition that the frequency responses of the primary and secondary paths corresponding to the feedback microphone do not match to the ones corresponding to the human ear. The influence of such mismatches on the performance are analyzed first, and then an optimization method is proposed to enhance the comprehensive performance at the human ear. In the proposed method, the feedback loop is constructed directly with the feedback microphone and any extra filters of the virtual sensing techniques are avoided. Cascade biquad filters are used as the controller, which is in accordance with current applications. A differential evolution algorithm was used to solve the proposed optimization problem, and the optimal parameters of the controller were found. It has been shown by the experimental results that, at the dummy head ear position, good noise reduction performance could be obtained at the low frequency band with limited noise enhancement for high frequencies, even if large frequency response mismatches exist.