Exhaustive Search Procedure Research Articles

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

Existing studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

A Novel Symbol Detection Strategy based on Discrete Elephant Herding Optimization Algorithm for MIMO-FBMC/OQAM System

The rapidly growing technological enhancements in wireless communication have pushed the researchers to develop various transmission schemes in recent decades. Multiple-input multiple-output - filter bank multicarrier/offset quadrate amplitude modulation (MIMO-FBMC/OQAM) is one of these schemes with exclusive features. However, unless an efficient symbol detector is employed, we cannot fully benefit from the superior features of MIMO-FBMC/OQAM system as the symbol vectors cannot be detected accurately at the receiver side. An ideal symbol detector is expected to have the capability of recovering the symbol vectors in the most accurate way without causing too much enhancement in the computational complexity. To meet this expectation, we have modified the conventional maximum likelihood (ML) strategy. Instead of using an exhaustive search procedure, which guarantees the highest detection performance but causes an excessive computational load in the ML scheme, an efficient metaheuristic algorithm called discrete elephant herding optimization (DEHO) was employed for optimizing the symbol vectors. With the integration of DEHO to the conventional ML in the aforementioned way, we have not only obtained a considerable reduction in the complexity of ML, but also reached the near-optimal symbol detection performance by outperforming the other symbol detectors considered in this paper.

Computational intelligence for early detection of infertility in women

Polycystic Ovary Syndrome (PCOS) is a major cause of female infertility. Early detection and treatment are critical in improving patients' prognoses. The current fertility conditions in India are sceptic, with women being more vulnerable. PCOS is one of the leading causes of infertility, affecting up to 20% of women in India. This necessitates an accurate and timely diagnosis, which can be accomplished by developing automated diagnosing models. Given that the data to be dealt with includes both clinical and non-clinical inputs, the effective information must be considered alone for the diagnosis. This necessitates a careful selection of features prior to diagnosing. The objective of this work is to identify prominent features in the dataset to develop a reliable yet simple diagnostic model to detect PCOS. To develop such a model, swarm intelligence (SI) is used for feature selection and machine learning (ML) is used for classification. Initially, optimal features are chosen using statistical methods such as correlation and Chi Square tests, as well as an exhaustive search procedure based on recursive elimination. Additionally, the SI algorithms, Particle Swarm Optimization (PSO), and Flashing Firefly (FF) algorithms are used to determine the optimal number and feasible combination of features. In the ML model, Random Forest (RF) and XGBoost classifiers were used for classification. The Synthetic Minority Oversampling Technique for Nominal and Continuous Features (SMOTENC) was employed to create a balanced dataset because the original dataset was unbalanced and all of the investigations were carried out for original and balanced datasets. Based on the metrics accuracy of training and testing, precision, recall, F1-score, AUC-ROC, and Matthew's Correlation Coefficient (MCC), a comparative analysis of the results is discussed and validated. The findings show that ML models with various feature selection algorithms perform best for various feature dimensions, with the model with RF classifier and features selected by PSO performed best with highest accuracy. A user interface module was developed with minimum features to diagnose PCOS. The performance of the model was also compared with the state of the art deep learning techniques.

Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations

One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of 4×4 dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper.

Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures

Concerning the growth rate in Renewable Energy share, especially in the EU, it is fact that Rooftop PV systems have a significant role to play. Nevertheless, it is also accepted that with current policies, schemes and scientific methods, today's Renewable Energy growth rate is still far from the desired one. Based on that, this study proposes a novel integration of a Mixed-Integer Linear Programming based model in a Public Tender Procedure, which enables the bulk installation and thus, the rapid expansion of Rooftop PV systems, in a least-cost manner. The main objective and contribution of this paper is to provide an economic solution to interested Public Bodies for utilizing the rapid growth of rooftop PV systems through novel mechanisms and hence, to better achieve the desired Renewable Energy targets. To further highlight the capabilities of the proposed method, the results from a real-life project, showed that the bulk installation of PV systems, in 404 public schools with a total PV capacity of 4.8 MWp, can be realized at an optimum cost. The main achievements are: (i) high-level competition, due to the ability of the proposed model to select multiple PV Installers, (ii) a 35% lower project cost compared to the initial estimated budget and (iii) a significantly lower unit cost of about 0.7 million €/MWp, compared to about 0.91 and 1.54 million €/MWp obtained from other similar projects. Finally, the outcomes of the proposed MILP model are validated by those obtained from an exhaustive optimal solution search procedure.

Efficient Match-Based Candidate Network Generation for Keyword Queries Over Relational Databases

Several systems proposed for processing keyword queries over relational databases rely on the generation and evaluation of Candidate Networks (CNs), i.e., networks of joined database relations that when processed as SQL queries, provide a relevant answer to the input keyword query. Although the evaluation of CNs has been extensively addressed in the literature, the problem of generating CNs efficiently and effectively has received much less attention. This challenging problem consists of automatically locating relations in the database that may contain relevant pieces of information, given a handful of keywords, and determining suitable ways of joining these relations to satisfy the implicit information needs expressed by a user while formulating his/her query. In this paper, we propose a novel approach for generating CNs, wherein the possible matches for the query in the database are efficiently enumerated at first. These <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query matches</i> are then used to guide the CN generation process, avoiding the exhaustive search procedure used by the current state-of-art approaches. We show that our approach allows the generation of a compact set of CNs that leads to superior quality answers, and demands less resources in terms of processing time and memory. These claims are supported by a comprehensive set of experiments that we carried out using several query sets and datasets used in previous related works and whose results we report and analyze here.

Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds.

Design of inductively degenerated common source RF CMOS Low Noise Amplifier

The Low Noise Amplifier (LNA) is the first stage in RF CMOS receivers. The Common Gate (CG) LNA and Inductively Degenerated Common Source (CS) LNA are one of the widely used topologies for realizing RF CMOS receivers. The present work emphasizes a simple and exhaustive search procedure for the synthesis and analysis of CMOS CG and Inductively Degenerated CS LNA circuits. The width (W), gate source voltage ( $$ V_{gs} $$ ) and drain source voltage ( $$ V_{ds} $$ ) of the transistors constitute the design space in the circuit design. The design first involves the use of a circuit simulator (HSPICE) to obtain the small signal parameters of the circuit for various W, $$ V_{gs}$$ , and $$ V_{ds} $$ of the transistors and then to generate a Look-Up Table (LUT) for all design points using the obtained values. This LUT is used to meet the target performance specifications along with appropriate analytical expressions derived from the circuit in a numerical simulator (MATLAB). This will enable one to explore the whole design space quickly and fastly for arriving at the optimal values for the device dimensions, bias voltages and bias currents of the two LNA circuits. The design methodology is demonstrated by designing CG and Inductively Degenerated CS LNA circuits using 90 nm CMOS technology library in which Inductively Degenerated CS LNA gets high gain and low noise figure than CG LNA.

Bi-objective optimization for tolerance allocation in an interchangeable assembly under diverse manufacturing environment

This paper presents bi-criteria formulation of a tolerance allocation model for an interchangeable assembly to simultaneously evolve suitable combination of manufacturing facility in multiple facility shaft-hole production environments of medium- and large-scale industries and tolerances to complement the need of customers. An Exhaustive Search Procedure is used to obtain the optimal solution for small and medium size problems and simulated annealing algorithm is used for large size problems. The usefulness of the Pareto front in manufacturing tolerance allocation decisions is demonstrated with three case study problems. The effect of process capability of shaft-hole assembly manufactured from alternative manufacturing machines and the optimality is analyzed in three cases to understand their criticality in decision-making. The models discussed in this paper could be useful for medium- and large-scale manufacturing industries, where there will be a variety of manufacturing facilities (specifications, capabilities, models, and types) for making both shaft-hole assembly and play a key role to meet the tolerance and cost requirements of different customers. This paper further discusses how this formulation and methodologies can be used for two hole and two shaft assemblies and multiple shaft-hole assemblies. Finally, the paper ends with highlighting directions of future research avenues in the shaft-hole assembly.

Optimization design of the CUSUM and EWMA charts for autocorrelated processes

AbstractThe traditional control charts produce frequent false alarm signals in the presence of autocorrelation. The implementation of the modified chart scheme is a way of handling the problem of autocorrelation in control charts. In modified charts, the standard control limits of the traditional charts are adjusted to offset the influence because of the autocorrelation. The exponentially weighted moving average– and cumulative sum–modified charts are 2 widely used charts for monitoring autocorrelated data. These charts have design parameters in their formulation, and the choice of these parameters play significant roles in the detection of out‐of‐control situations. In reality, the magnitude of the mean shift is uncertain, and this leads to a difficulty in the choice of design parameters by the practitioner. The use of optimal parameters can enhance process performance in such situations. In this paper, we determine optimal design parameters for the charts using an exhaustive search procedure. In the optimization process, we determine the parameters that produce the smallest extra quadratic loss (EQL) value for each autocorrelation coefficient. This criterion measures the anticipated loss attributed to poor quality in the process. The loss in quality is lowered by minimizing the EQL and the combination of parameters in the chart that yields the smallest EQL has a better detection ability over the entire shift range. For the purpose of this work, we concentrate on autocorrelation that can be specifically modelled with autoregressive models. This article provides the practitioner with optimal parameters that can be used to enhance the overall effectiveness of the charts over an entire shift range.

Computationally Efficient Blind Estimation of Carrier Frequency Offset for MIMO-OFDM Systems

In this paper, we propose a new computationally efficient blind carrier frequency offset (CFO) estimator for multi-input multi-output orthogonal frequency division multiplexing systems. A cost function is carefully designed and can be exactly expressed as the superposition of very few harmonically related cosine waves even with the effect of the noise. Using this property, the minimization of the designed cost function can be solved in a quite computationally efficient manner without any exhaustive grid search procedure. It is seen that the proposed estimator can achieve comparable estimation performance as existing competitors with substantially reduced computational burden. Moreover, in order to improve the estimation performance, we further develop an enhanced CFO estimator by introducing an additional one-step adjustment. We find that the enhanced estimator can attain almost the same estimation performance as the existing maximum likelihood estimator but with a lower order of computational burden. We provide both numerical results and theoretical performance analysis to corroborate the proposed studies.

Combining exhaustive search and multi-objective evolutionary algorithm for service restoration in large-scale distribution systems

Service restoration problem in distribution systems emerges after the faulted areas have been identified and isolated. A solution is obtained by determining the minimal number of switching operations that results in a configuration with a minimal number of healthy out-of-service areas without violating the operational and radiality constraints. Recently a practical and efficient methodology was developed and demonstrated through tests performed on the real and large-scale distribution system of Londrina city (Brazil). This methodology, named MEAN-MH, combines a Multi-objective Evolutionary Algorithm with Node-Depth Encoding, Multiple criteria tables and an alarming Heuristic. As any methodology based on meta-heuristics, the MEAN-MH does not guarantee to find the optimal solution of the service restoration problem, even when the optimal solution requires operations only in normally open switches incident to the healthy out-of-service areas (named as Tier 1 normally open switches). This paper proposes an extension of MEAN-MH that incorporates an Exhaustive Search (ES) procedure as a previous stage before MEAN-MH. The proposed ES guarantees the generation and analysis of all possible radial configurations that restore the service to all healthy out-of-service areas requiring operations only in Tier 1 normally open switches. Therefore, when the optimal solution of the service restoration problem requires operations only in Tier 1 NO switches, the proposed methodology, named MEAN-MH+ES, guarantees the optimum. However, when the optimal solution requires operations also in other switches, the MEAN-MH+ES searches by a feasible solution minimizing both the number of switching operations and the number of healthy out-of-service areas. To demonstrate the effectiveness of the proposal, both MEAN-MH and MEAN-MH+ES are applied to two real and large-scale distribution systems of Brazil. Moreover, the results obtained by MEAN-MH+ES are compared with those found in another published work.

Optimal Feature Selection in High-Dimensional Discriminant Analysis.

We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the ℓ2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable selection performance of these procedures have not been established, even though model interpretation is of fundamental importance in scientific data analysis. This paper bridges the gap by providing sharp sufficient conditions for consistent variable selection using the sparse discriminant analysis (Mai et al., 2012). Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size n, dimensionality p, and sparsity level s in the high-dimensional setting. Sufficient conditions are complemented by the necessary information theoretic limits on the variable selection problem in the context of high-dimensional discriminant analysis. Exploiting a numerical equivalence result, our method also establish the optimal results for the ROAD estimator (Fan et al., 2012) and the sparse optimal scaling estimator (Clemmensen et al., 2011). Furthermore, we analyze an exhaustive search procedure, whose performance serves as a benchmark, and show that it is variable selection consistent under weaker conditions. Extensive simulations demonstrating the sharpness of the bounds are also provided.

The Square Root Multipliers Algorithm for Discrete Capacity and Buffer Assignment problems in Elastic Traffic Networks

This paper discusses a novel approach for the dimensioning of packet networks, under the constraints of end-to-end Quality-of-Service (QoS) requirements. The network modeling also considers the dynamic behavior of today's networks traffic. In particular, the proposed approach considers multilink networks where discrete capacities are the decision variables. This Discrete Capacity Assignment (DCA) problem can be classified as a constrained combinatorial optimization problem and therefore it is NP-complete. In order to solve the problem, we propose the Square Root Multipliers Algorithm (SRMA) followed by a Randomized Rounding (RR) method. We also enforce loss probability constraints by properly choosing buffer sizes, therefore facing the Buffer Assignment (BA) problem. To evaluate the performance of our approach, SRMA/RR results are compared with the optimal solutions found by an exhaustive search (ES) procedure. Analytical results for a variety of problem instances are reported, and a verification is performed based on simulations conducted with the software NS-2. The results suggest that the proposed approach provides a quite efficient method to obtain near-optimal solutions with small computational effort.

Novel hybrid heuristics for an extension of the dynamic relay deployment problem over disaster areas

In this paper, we propose a novel autonomous intelligent tool for the optimum design of a wireless relayed communication network deployed over disaster areas. The so-called dynamic relay deployment problem consists of finding the optimum number of deployed relays and their location aimed at simultaneously maximizing the overall number of mobile nodes covered and minimizing the cost of the deployment. In this paper, we extend the problem by considering diverse relay models characterized by different coverage radii and associated costs. To efficiently tackle this problem we derive a novel hybrid scheme comprising: (1) a Harmony Search (HS)-based global search procedure and (2) a modified version of the well-known K-means clustering algorithm as a local search technique. Single- and bi-objective formulations of the algorithm are proposed for emergency and strategic operational planning, respectively. Monte Carlo simulations are run over a emulated scenario based on real statistical data from the Castilla La Mancha region (center of Spain) to show that, in comparison with a standard implementation of the K-means algorithm followed by a exhaustive search procedure over all relay-model combinations, the proposed scheme renders on average better coverage levels and reduced costs providing, at the same time, an intelligent tool capable of simultaneously determining the number and models of the relays to be deployed.

PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy

Low-dose electron microscopy of cryo-preserved individual biomolecules (single-particle cryo-EM) is a powerful tool for obtaining information about the structure and dynamics of large macromolecular assemblies. Acquiring images with low dose reduces radiation damage, preserves atomic structural details, but results in low signal-to-noise ratio of the individual images. The projection directions of the two-dimensional images are random and unknown. The grand challenge is to achieve the precise three-dimensional (3D) alignment of many (tens of thousands to millions) noisy projection images, which may then be combined to obtain a faithful 3D map. An accurate initial 3D model is critical for obtaining the precise 3D alignment required for high-resolution (<10Å) map reconstruction. We report a method (PRIME) that, in a single step and without prior structural knowledge, can generate an accurate initial 3D map directly from the noisy images.

Nonlinear Load-Deflection Behavior of Reinforced Concrete Drilled Piles in Stiff Clay

AbstractThe results of three full-scale lateral load tests of cast-in-drilled-hole RC piles are evaluated to provide insights into the soil-pile interaction behavior as represented by p-y curves (p = soil reaction/length; y = lateral pile deflection) spanning from elastic conditions to mobilization of the soil capacity. The test sequence enables evaluation of the diameter effects on the p-y behavior as a result of testing two otherwise similar specimens, but with different diameters (0.6 and 1.8 m), and evaluation of the head-fixity effects as a result of testing similar specimens (with a 0.6 m diameter) under flagpole and fixed-head conditions. A constrained exhaustive search procedure that facilitates data interpretation even in the presence of severe shaft nonlinearity is introduced, which was needed because the testing was performed to full structural failure conditions. The p-y curves obtained with the present test data differ from the predictions of a standard (American Petroleum Institute) model, i...

Optimization-based Domain Reduction in Guaranteed Parameter Estimation of Nonlinear Dynamic Systems

This paper is concerned with guaranteed parameter estimation in nonlinear dynamic systems in a context of bounded measurement error. The problem consists of finding—or approximating as closely as possible—the set of all possible parameter values such that the predicted outputs match the corresponding measurements within prescribed error bounds. An exhaustive search procedure is applied, whereby the parameter set is successively partitioned into smaller boxes and exclusion tests are performed to eliminate some of these boxes, until a prespecified threshold on the approximation level is met. In order to enhance the convergence of this procedure, we investigate the use of optimization-based domain reduction techniques for tightening the parameter boxes before partitioning. We construct such bound-reduction problems as linear programs from the polyhedral relaxation of Taylor models of the predicted outputs. When applied to a simple case study, the proposed approach is found to reduce the computational burden significantly, both in terms of CPU time and number of iterations.