Discrete Optimization Techniques Research Articles

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Dys-R Questionnaire: A Novel Screening Tool for Dysbiosis Linked to Impaired Gut Microbiota Richness.

Practical and affordable tools to screen intestinal dysbiosis are needed to support clinical decision making. Our study aimed to design a new subjective screening tool for the risk of intestinal dysbiosis from a previously described nonvalidated questionnaire (DYS/FQM) and based on subjective and objective data. A total of 219 individuals comprised the chronic diseases (CD; n = 167) and healthy control (HC; 52 subjects) groups. Sociodemographic, anthropometric, body composition, lifestyle, past history, intestinal health, and dietary data were collected. The gut microbiota (GM) profile was assessed from fecal samples using the 16S rRNA sequencing. Scores for the new tool (Dys-R Questionnaire) were assigned using discrete optimization techniques. The association between Dys-R scores and dysbiosis risk was assessed through correlation, simple linear models, sensitivity, specificity, as well as positive and negative predictive values. We found significant differences in the Chao1 Index between CD and HC groups (adjusted p-value = 0.029), highlighting lower GM richness as the primary marker for intestinal dysbiosis. DYS/FQM showed poor performance in identifying poor GM richness. Dys-R exhibited a 42% sensitivity, 82% specificity, 79% positive predictive value (PPV), and 55% negative predictive value (NPV) to identify poor GM richness. The new Dys-R questionnaire showed good performance in ruling out dysbiosis.

Lightweight Design Method for Three-Wheeled Mobility Frame Using Discrete Material and Thickness Optimization Technique

Lightweight Design Method for Three-Wheeled Mobility Frame Using Discrete Material and Thickness Optimization Technique

Size optimization design of members for shear wall high-rise buildings

Structural optimization can lead to efficient designs for high-rise concrete buildings. However, the high computational cost of the structural analysis step hinders its application in practical cases. With regards to this problem, a novel hybrid optimization framework is developed in this study. Discrete variables, code-stipulated constraints and other construction requirements under multiple loading scenarios are considered. The hybrid optimization framework contains two steps: in the first step, a computationally efficient approximation of the optimization formulation is constructed using a combined response surface methodology (RSM). Combining all the design constraints, three sub-optimization problems are proposed based on the relaxation model of the original problem to update the response surface model in the near-optimum region. When the response surface model has sufficient accuracy in the near-optimum region, in the second step, a discrete Particle Swarm Optimization (PSO) technique is applied to search for the optimal solution utilizing the response surface model. By integrating the commercially available ETABS, a dedicated optimization software with an independent interface is developed and details for practical software development are included in this paper. Three examples of high-rise concrete building design are presented to demonstrate the effectiveness of the proposed optimization framework, and the results are compared to those of a general PSO algorithm. It is shown that, compared to the conventional design approach, a 14.3% reduction in material usage is achieved by the hybrid optimization framework. Additionally, the proposed framework significantly improves the computational efficiency compared to PSO. This methodology can therefore be used by engineers to optimize member sizes for high-rise buildings so that stable and sustainable designs can be achieved.

Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data

Feature selection (FS) is commonly thought of as a pre-processing strategy for determining the best subset of characteristics from a given collection of features. Here, a novel discrete artificial gorilla troop optimization (DAGTO) technique is introduced for the first time to handle FS tasks in the healthcare sector. Depending on the number and type of objective functions, four variants of the proposed method are implemented in this article, namely: (1) single-objective (SO-DAGTO), (2) bi-objective (wrapper) (MO-DAGTO1), (3) bi-objective (filter wrapper hybrid) (MO-DAGTO2), and (4) tri-objective (filter wrapper hybrid) (MO-DAGTO3) for identifying relevant features in diagnosing a particular disease. We provide an outstanding gorilla initialization strategy based on the label mutual information (MI) with the aim of increasing population variety and accelerate convergence. To verify the performance of the presented methods, ten medical datasets are taken into consideration, which are of variable dimensions. A comparison is also implemented between the best of the four suggested approaches (MO-DAGTO2) and four established multi-objective FS strategies, and it is statistically proven to be the superior one. Finally, a case study with COVID-19 samples is performed to extract the critical factors related to it and to demonstrate how this method is fruitful in real-world applications.

Bi-level energy optimization model in smart integrated engineering systems using WSN

Based on fuzzy relational inequality, a bi-level linear programme optimizes the visible light brightness and operating costs of access points in a wireless transmission station system. Consider the first computing problem utilizing a minimum solution matrix. A convex infinite set is generated by a restricted number of closed intervals. Second, computing is an objective-domain nonlinear mathematical optimization problem. A multi-objective optimization problem is used to solve the second programming challenge. The constraint set must be used. Use discrete optimization techniques and branch-and-bound procedures for “digital integer linear programming”. Our technique has been shown to be both practical and successful. The programming complexity increases as the organization expands.

An Expert-Assisted Chest Radiograph Classification Tool To Differentiate Between Active Tuberculosis and Healed Tuberculosis Sequelae

Tuberculosis (TB) heals with a scarring response in the lung and chest radiographic abnormalities termed as tuberculosis sequelae are often observed in patients with healed pulmonary TB. Patients with healed TB can also have frequent chest infections, breathlessness and fever, which are also seen in active pulmonary TB. This leads to frequent mismanagement of these patients where they are treated as active TB cases. In this paper, we present a method to differentiate between active and healed pulmonary TB patients using their chest radiograph images. Our method requires the clinician to acquire the image of the chest radiograph using a hand-held cellular smartphone, and then mark points along the boundary of the lungs in the image. We then use these point markings in an iterative Otsu-based thresholding method, parameterized optimally using a discrete simulation optimization technique, to segment the lungs from the rest of the image. Clinically relevant morphological features such as the lung areas, lung base curvatures and tracheal shift, and textural features such as average intensity within the lung, are extracted from the segmented image. We then train multiple standard machine learning methods on the extracted features, and convolutional neural networks on the segmented images themselves to classify the radiograph images. We achieve area under the receiver operating characteristic curve scores of up to 0.83. We also present results from preliminary experiments conducted to estimate the extent of inter-doctor variability in marking points on the lung boundaries.

An integer programming model to assign patients based on mental health impact for tele-psychotherapy intervention during the Covid\u201319 emergency

The Covid–19 pandemic challenges healthcare systems worldwide while severely impacting mental health. As a result, the rising demand for psychological assistance during crisis times requires early and effective intervention. This contributes to the well-being of the public and front-line workers and prevents mental health disorders. Many countries are offering diverse and accessible services of tele-psychological intervention; Ecuador is not the exception. The present study combines statistical analyses and discrete optimization techniques to solve the problem of assigning patients to therapists for crisis intervention with a single tele-psychotherapy session. The statistical analyses showed that professionals and healthcare workers in contact with Covid–19 patients or with a confirmed diagnosis had a significant relationship with suicide risk, sadness, experiential avoidance, and perception of severity. Moreover, some Covid–19-related variables were found to be predictors of sadness and suicide risk as unveiled via path analysis. This allowed categorizing patients according to their screening and grouping therapists according to their qualifications. With this stratification, a multi-periodic optimization model and a heuristic are proposed to find an adequate assignment of patients to therapists over time. The integer programming model was validated with real-world data, and its results were applied in a volunteer program in Ecuador.

Power System Controlled Islanding using Modified Discrete Optimization Techniques

Controlled islanding is implemented to save the power system from experiencing blackouts during severe sequence line tripping. The power system is partitioned into several stand-alone islands by removing the optimal transmission line during controlled islanding execution. Since selecting the optimal transmission lines to be removed (cutsets) is important in this action, a good technique is required in order to determine the optimal islanding solution (lines to be removed). Thus, this paper developed two techniques, namely Modified Discrete Evolutionary Programming (MDEP) and Modified Discrete Particle Swarm Optimization (MDPSO) to determine the optimal islanding solution for controlled islanding implementation. The best technique among these two which is based on their capability of producing the optimal islanding solution with minimal objective function (minimal power flow disruption) will be selected to implement the controlled islanding. The performance of these techniques is evaluated through case studies using the IEEE 118-bus test system. The results show that the MDEP technique produces the best optimal islanding solution compared to the MDPSO and other previously published techniques.

A Unified Framework for Simultaneous Layout Decomposition and Mask Optimization

In advanced technology nodes, layout decomposition (LD) and mask optimization (MO) are two key stages in integrated circuit design. Due to the inconsistency of the objectives of these two stages, the performance of conventional layout and MO may be suboptimal. To tackle this problem, in this article, we propose a unified framework, which seamlessly integrates LD and MO. We propose a gradient-based approach to solve the unified mathematical formulation, as well as a set of discrete optimization techniques to avoid being stuck in local optimum. The conventional optimization process can be accelerated as some inferior decomposition results can be smartly pruned in early stages. The experimental results show that the proposed unified framework can achieve more than $34\times $ speed-up compared with the conventional two-stage flow, meanwhile, it can dramatically reduce EPE violations by more than $8\times $ , and thus maintain better design quality.

Topology Optimization of Metal and Carbon Fiber Reinforced Plastic (CFRP) Laminated Battery-Hanging Structure.

This study addressed the topology optimization of a carbon fiber reinforced plastic (CFRP) laminated battery-hanging structure of an electric vehicle. To accommodate parameterization for thickness and orientation of CFRP materials, the discrete material and thickness optimization (DMTO) technique was adopted. To include metal material as a reinforcement structure into the optimization simultaneously, the DMTO technique was extended here to achieve concurrent optimization of CFRP thickness topology, CFRP orientation selection and the topology of the metal reinforcement plate. Manufacturing constraints were applied, including suppressing intermediate void across the thickness direction of the laminate, contiguity constraint and the symmetrical layers. Sensitivities of the objective function were derived with respect to design variables. To calculate analytical sensitivities, finite element analysis was conducted and strain vectors were exported from a commercial software (ABAQUS) into a mathematical analysis tool (MATLAB). The design objective was to minimize the local displacement subject to the constraints of manufacturing and mass fraction. The mechanical performance of the optimized CFRP structure was compared with the original steel structure. To validate the optimization results, a prototype of the CFRP battery-hanging structure was fabricated and experimental testing was conducted. The results show that the total mass of the CFRP battery-hanging structure was reduced by 34.3% when compared with the steel one, while the mechanical property was improved by 25.3%.

Visibility-Based Persistent Monitoring of Piecewise Linear Features on a Terrain Using Multiple Aerial and Ground Robots

Persistent monitoring on terrains using mobile robotic sensors requires coordinated planning. Terrain features add visibility obstacles and limited fuel capacity of aerial robots leads to range restrictions that make the problem challenging. We address the visual-monitoring problem on piecewise linear features within a terrain using multiple mobile robots for persistent operations. The planner must account for visual coverage, refueling aerial robots during the mission, and placement of refueling depots while also utilizing the available sensor diversity to minimize overall costs for the monitoring mission. Building on previous works on visibility in specific classes of polygons and fuel-constrained routing, we develop a discrete representation of the problem that allows the design and application of discrete optimization techniques to find optimal solutions. We develop a mixed-integer linear programming (MILP) formulation and discuss a branch-and-cut implementation to compute exact solutions. We also develop a construction heuristic based on the idea of competitive construction of robot paths using a step-increment strategy. We report the results from computational simulations and illustrate proof of concept using experiments on real robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the need to perform persistent monitoring in applications, such as border patrol and perimeter surveillance. Unmanned aerial and ground robots can be used to perform these activities uninterruptedly. However, aerial robots have limited fuel capacity and need periodic refueling. Hence, the number of refueling depots and their placement within the environment also affects the monitoring task. Also, due to terrain variation, robots are subject to limited visibility. Therefore, we need to consider refueling constraints and terrain visibility aspects while planning optimal routes for the robots to perform visual monitoring. In this article, we present a general optimal routing formulation to compute exact solutions. We also present a fast heuristic for real-time applications that produce feasible solutions. The algorithms are validated in simulations. We also show a proof of concept using experiments in limited outdoor settings.

Algorithms for tensor network contraction ordering

Contracting tensor networks is often computationally demanding. Well-designed contraction sequences can dramatically reduce the contraction cost. We explore the performance of simulated annealing and genetic algorithms, two common discrete optimization techniques, to this ordering problem. We benchmark their performance as well as that of the commonly-used greedy search on physically relevant tensor networks. Where computationally feasible, we also compare them with the optimal contraction sequence obtained by an exhaustive search. Furthermore, we present a systematic comparison with state-of-the-art tree decomposition and graph partitioning algorithms in the context of random regular graph tensor networks. We find that the algorithms we consider consistently outperform a greedy search given equal computational resources, with an advantage that scales with tensor network size. We compare the obtained contraction sequences and identify signs of highly non-local optimization, with the more sophisticated algorithms sacrificing run-time early in the contraction for better overall performance.

Comments on: Perspectives on integer programming for time-dependent models

This paper presents an updated and comprehensive review of discrete optimization techniques for solving time-dependent problems, i.e. decision problems in which activities and resources have to be scheduled over time. Known compact models with continuous variables representing arrival/service/departure times are either (i) nonlinear or (ii) linear with a weak relaxation. This is why practitioners (and a consistent part of the scientific literature) make use of a discretization of time which introduces an approximation. Such extended Integer Programming (IP) formulations rely on binary variables indexed by time (time-indexed models, TI). Their size may be huge, and so they tend to be out of reach for the current IP solver technology.

A hybrid weed optimized coverage path planning technique for autonomous harvesting in cashew orchards

A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards. The main challenge in such an orchard is the collection of fruits and nuts lying on the floor. The manual collection of fruits and nuts is both time consuming and labour intensive. The scenario begs for automated collection of fruits and nuts. There are methods developed in research for continuous crop fields, but none for discrete coverage. The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated. A novel Mahalanobis distance based partitioning approach for performing coverage is introduced. The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions. Optimization of the generated paths is achieved through a combination of local and global search techniques. This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator. A case study is formulated for the fruit picking operations in the orchards of Puducherry. The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate, convergence diversity and deviation ratio. The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively. The deviation ratio was 0.02 percent and 2.16 percent, with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes. The achieved solutions was on par with the global best solutions achieved so far for the test datasets.

Discrete material optimization for wave-based acoustic design

The problem of automatic design of acoustic spaces is prevalent in architecture and room acoustics. We present a novel algorithm to automatically compute the optimal materials of large architectural spaces. Our method uses discrete optimization techniques to determine the best material configuration for desired acoustic properties of a structure, while taking into account properties of real-world materials. An efficient acoustic wave solver is used to accurately compute the acoustic impulse responses that drive the optimization process. Our method is tested on various computer representations of real-world scenes where we show how new material characteristics can be computed to improve the scene's strength, clarity, and reverberation time.

Robust topology optimization for multiple fiber-reinforced plastic (FRP) composites under loading uncertainties

This study proposes a non-deterministic robust topology optimization of ply orientation for multiple fiber-reinforced plastic (FRP) materials, such as carbon fiber–reinforced plastic (CFRP) and glass fiber–reinforced plastic (GFRP) composites, under loading uncertainties with both random magnitude and random direction. The robust topology optimization is considered here to minimize the fluctuation of structural performance induced by load uncertainty, in which a joint cost function is formulated to address both the mean and standard deviation of compliance. The sensitivities of the cost function are derived with respect to the design variables in a non-deterministic context. The discrete material optimization (DMO) technique is extended here to accommodate robust topology optimization for FRP composites. To improve the computational efficiency, the DMO approach is revised to reduce the number of design variables by decoupling the selection of FRP materials and fiber orientations. In this study, four material design examples are presented to demonstrate the effectiveness of the proposed methods. The robust topology optimization results exhibit that the composite structures with the proper ply orientations are of more stable performance when the load fluctuates.

Cost-Driven Scheduling for Deadline-Based Workflow Across Multiple Clouds

With the development of cloud computing, the coexistence of multiple cloud service providers appears in the current cloud market. Due to heterogeneous instance types, different bandwidths and various price models among multiple clouds, it is a challenging issue to schedule a deadline-constrained scientific workflow across multiple clouds. Existing research for workflow scheduling are mostly in the traditional distributed computing environment (such as grid), and only a few primal contributions are made in the cloud environment. This paper proposes a scheduling strategy for a deadline-constrained scientific workflow across multiple clouds. In order to minimize the execution cost of the workflow while meeting its deadline, our strategy utilizes the discrete particle swarm optimization technique, and adopts randomly two-point crossover operator and randomly single point mutation operator of the genetic algorithm. Besides, the strategy optimizes the performance for both computation cost and data transfer cost across multiple clouds. Our strategy is evaluated through well-known workflows, and experimental results show that it performs better than other state-of-the-art strategies.

Binary Bat Algorithm Applied to Solve MISO-Type PID-SSSC-Based Load Frequency Control Problem

In this paper, a novel nature-inspired population-based discrete optimization technique, namely binary bat algorithm (BBA), is proposed for the first time to solve load frequency control (LFC) problem in power system. Initially, bat algorithm (BA) is designed by imitating the echolocation behavior of bats in nature and then binary approach has been hybridized to accelerate the convergence rate. To demonstrate the effectiveness of BBA, two nonlinear test systems, namely three-area and four-area hydrothermal power plants, are considered for design and analysis. To damp out frequency and tie-line power oscillations due to sudden load perturbation, optimized proportional-integral-derivative (PID)-type multi-input–single-output (MISO) static synchronous series compensator (SSSC) is designed and integrated in LFC loop. The controller gains are concurrently optimized by BBA employing integral square error (ISE)- and integral time absolute error (ITAE)-based fitness functions. The potency of the proposed scheme is proven among other well-known evolutionary techniques and intelligent controllers like fuzzy logic, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) by transient analysis method. The simulation results presented in this article clearly show that assimilation of BBA-tuned MISO-type PID-SSSC controller in LFC loop can curtail the system oscillations actively by contributing fast compensation. Finally, robustness of the designed controller is validated against random load perturbation (RLP).

Using Contingent Valuation and Numerical Methods to Determine Optimal Locations for Environmental Facilities: Public Arboretums in South Korea

Because the social benefit generated from a public facility varies according to its accessibility, population density of location, local income level, local taste, and the existence of similar nearby facilities, the value of facilities can vary depending on their location. Therefore, determining the ideal location is an important problem. This study provides a new direction for solving this problem by combining the contingent valuation (CV) method and nested partitions (NP) algorithm using siting of public arboretums as an example. First, consumers' willingness to pay for environmental facilities considering household characteristics and distance to both newly built and existing facilities are estimated using the CV method. In this process, consumers are divided into two groups (active and passive users), assuming they have different willingness to pay for the facilities. Then, using the results of the CV, the NP algorithm, a simulation-based discrete optimization technique, is constructed to efficiently identify optimal locations that maximize social benefit considering regional characteristics. The results of the proposed algorithm exceeded the performance of the benchmark case, and this study's findings can be used to aid decisions about complicated multiple facility locations. Moreover, visualized results are provided, which can be useful for local and central decision makers.