Multiple Performance Metrics Research Articles

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signal-to-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection.

A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria.

A framework for generating and evaluating façade designs using a multi-agent system approach

Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.

The significance of periodic parameters for ANN modeling of daily SO2 and NOx concentrations: A case study of Belgrade, Serbia

In recent decades, artificial neural networks (ANNs) have been used for the prediction of concentration of air pollutants in urban areas. Beside meteorological variables, periodic parameters, such as hour of the day or month of the year, have been frequently used to improve the performance of ANN models by representing variations of emission sources. In this paper, different forms of periodic parameters, i.e. smoothed cosines based approximation and normalized historical mean values, were combined with meteorological variables in order to analyze the sensitivity of the ANN model to them. Ward neural network and general regression neural network were used and compared for the prediction of daily average concentrations of SO2 and NOx in Belgrade, Serbia. Multiple performance metrics have demonstrated that models based on periodic parameters outperform the corresponding models that used only meteorological variables as inputs. Also, a newly proposed normalized historical mean MOYnmv (month of the year) proved to be more appropriate in majority of cases than the traditional cosines based approximation (MOYcos). A simple rule for the selection of the most efficient MOY form was defined depending on their mutual correlation (r). Results have shown that if MOYnmv is correlated with MOYcos with r > 0.8, then ANN models what uses MOYnmv provide more accurate predictions.

The Neglected “R” in the Risk-Needs-Responsivity Model: A New Approach for Assessing Responsivity to Correctional Interventions

Prevailing correctional practice holds that offenders should be assigned to interventions on the basis of assessments for risk, needs, and responsivity. Assessments of responsivity, however, typically consist of little more than a checklist of items such as motivation, gender, language, or culture. We introduce a new actuarial approach for assessing responsivity, which focuses on predicting whether individuals will desist after participating in an intervention. We assess responsivity by using multiple classification methods and predictive performance metrics to analyze various approaches for prioritizing individuals for correctional interventions. The results suggest that adding an actuarial responsivity assessment to the existing risk and needs assessments would likely improve treatment assignments and further enhance the effectiveness of an effective intervention. We conclude by discussing the implications of more rigorous responsivity assessments for correctional research, policy, and practice.

Benchmarking optimization methods for parameter estimation in large kinetic models

MotivationKinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is difficult to choose the best one for a given problem a priori. A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently missing, and smaller studies provided contradictory findings.ResultsWe use a collection of benchmarks to evaluate the performance of two families of optimization methods: (i) multi-starts of deterministic local searches and (ii) stochastic global optimization metaheuristics; the latter may be combined with deterministic local searches, leading to hybrid methods. A fair comparison is ensured through a collaborative evaluation and a consideration of multiple performance metrics. We discuss possible evaluation criteria to assess the trade-off between computational efficiency and robustness. Our results show that, thanks to recent advances in the calculation of parametric sensitivities, a multi-start of gradient-based local methods is often a successful strategy, but a better performance can be obtained with a hybrid metaheuristic. The best performer combines a global scatter search metaheuristic with an interior point local method, provided with gradients estimated with adjoint-based sensitivities. We provide an implementation of this method to render it available to the scientific community.Availability and implementationThe code to reproduce the results is provided as Supplementary Material and is available at Zenodo https://doi.org/10.5281/zenodo.1304034.Supplementary information Supplementary data are available at Bioinformatics online.

Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models ( R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

A case-study based framework for assessing the multi-sector performance of green infrastructure

Green infrastructure is emerging as a holistic stormwater management strategy that can also provide multi-sector benefits. Robust demonstration of project success can help leverage the appeal of green infrastructure to different sectors and open the door to a variety of funding opportunities. Yet comprehensively assessing the performance of these natural systems can be challenging, especially when communicating the benefits to a wide variety of stakeholders. A cohesive, well-described assessment structure may promote a higher degree of investor confidence by more comprehensively monitoring and measuring green infrastructure success. This paper develops a conceptual framework that incorporates a robust assessment component for communicating with potential investors through the inclusion of multiple evaluation methods, performance metrics, and risk categories. The applied performance of this framework is then validated using fourteen U.S. and international case studies. We found that our framework fit a wide range of projects while maintaining a degree of flexibility that did not sacrifice specificity when applied to individual case studies. This suggests that: 1) some GI projects already incorporate one or more evaluation methods; 2) a number of highly specific metrics—particularly social and economic performance metrics—exist that are capable of capturing a wide-range of benefits that can be easily integrated into a framework; 3) the incorporation of risk and risk management technique identification could be emphasized to increase investor confidence; 4) at least some degree of standardization across projects exists already which can help future project implementers design GI strategies that best fit their needs.

Model Predictive Control as a Simulator for Studying the Operation Quality of Semiconductor Supply Chains in the Perspective of Integrated Device Manufacturers

The nature of competition in the semiconductor industry has shifted from inter-firm to inter-supply chain. For integrated device manufacturers (IDMs), effective supply chain management (SCM) has become their competitive advantage in the market with intensive rivalry. Model predictive control (MPC) is a convincingly robust and flexible technique for dynamically controlling material release, managing inventories, and meeting customer demand through supply chains. This study proposed an MPC framework for programming a nonstationary semiconductor manufacturing supply chain network in the perspective of IDMs. The supply chain network is characterized by multiple echelons from raw material supply to finished product shipment, multiple factories, manufacturing stochasticity, diverse product, and product substitution mechanisms. The MPC model was employed as a simulator to obtain the optimal operations of supply chain network under various combinations of three two-level scenario variables: high/low demand variation, push/pull supply chain strategy, and normal/quick raw material supply policy. The operation quality of supply chain and the effects of scenario variables were assessed based on multiple performance metrics.

The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates

AbstractThe first systematic comparison between Swarm‐C accelerometer‐derived thermospheric density and both empirical and physics‐based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10‐s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high‐latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere‐driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activity were better captured by the physics‐based model than the empirical models. It is concluded that Swarm‐C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research.

Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment. The process of evaluating machine learning models in Azure MLS has certain limitations, e.g. small number of performance metrics and lack of functionality to evaluate custom built regression models with R language. This paper reports the results of an effort to build an Enhanced Evaluate Model (EEM) module which facilitates and accelerates Azure experiments development and evaluation. The EEM combines multiple performance metrics allowing for multisided evaluation of the regression models. EEM offers 4 times more metrics than built-in Azure Evaluate Model module. EEM metrics include: CoD, GMRAE, MAE, MAPE, MASE, MdAE, MdAPE, MdRAE, ME, MPE, MRAE, MSE, NRMSE_mm, NRMSE_sd, RAE, RMdSPE, RMSE, RMSPE, RSE, sMAPE, SMdAPE, SSE. Also, EEM supports evaluation of the R language based regression models. The operational Enhanced Evaluate Model module has been published to the web and openly available for experiments and extensions.

A multimetric and multideme multiagent system for multiobjective optimization

AbstractThis article proposes a multiagent system consisting of a number of multiobjective metaheuristic agents (namely, multiobjective genetic algorithm, strength Pareto evolutionary algorithm, differential evolution, simulated annealing, and particle swarm optimization) working toward to extract optimal or very close‐to‐optimal Pareto fronts using multiple performance metrics in a sessionwise manner. At the beginning of each session, the main population is divided into a number of subpopulations, and each of them is assigned to a particular agent. The system runs in consecutive sessions such that, at the beginning of a session, agents start running after being assigned with a subpopulation and return the optimized subpopulations together with the corresponding set of nondominated solutions at the end of the session. There are 3 multiobjective assessment metrics in use, and a different metric is considered for each session to measure the success of each metaheuristic agent. The evaluation of individual agents using a particular assessment metric is used in 2 ways: first, the number of fitness evaluations for each agent is adjusted based on their performance; second, the subpopulation improved by an individual agent might be rejected on the basis of its evaluation score. At the end of each session, individual subpopulations are merged to get the updated main population, whereas individual sets of nondominated solutions are combined to form the global Pareto front. In addition to the individual multiobjective metaheuristic agents, the system also contains a number of coordination and synchronization agents that run the whole system toward its objectives. The proposed system is tested using real‐valued multiobjective benchmark problems in 2009 IEEE Congress on Evolutionary Computation. Experimental results and statistical evaluations exhibited that the achieved success is better than many of state‐of‐the‐art algorithms.

Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring

Fault diagnosis of the rotary machines is investigated through different kinds of signals. However, the literature shows that the vibration signal analysis is the most commonly used and effective approach. This research investigates the engine faults, including the misfire and valve clearance faults, using the vibration data captured by four sensors placed in different locations of the automobile engine and under different experimental circumstances. The application of the Fast Fourier Transform (FFT) is proposed as a feature extraction methodology which leads to the extraction of 16 features. In addition, four features are extracted using the acquired signals eigenvalues. The statistical approach is proposed to select features for classification of the engine’s state. The Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k Nearest Neighbor (kNN) classification algorithms are employed to predict if the motor works healthily based on the selected features and, if not, what kind of faults is in the engine. The performance of ANN, SVM, and kNN in fault diagnosis is analyzed considering different scenarios, features, and based on multiple performance metrics. Comparing the results with the similar efforts in the literature proves the validity of the proposed methods and highlights their superiorities.

Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces

This paper presents development and performance evaluation of a host-based misuse intrusion detection system. The misuse detection system employs an ensemble design for classification and N-gram feature extraction methodology for preprocessing the raw ADFA-LD system call trace data. Inputs to the e nsemble classifier are fixed size patterns whose attributes are N-grams derived from operating system call traces. The ADFA-LD data set entails system call traces generated by a number of concurrently executing applications on the Linux operating system environment. In addition to the normal or attack-free operating mode, six different attacks are modeled in the data set. Two filters are used to capture the unique signatures of each class and also to reduce the dimensionality of input patterns. The most frequent unique features in the form of N-grams from each class are extracted from the raw trace data. Then, to eliminate the effect of noise, most frequent patterns regardless of uniqueness are extracted based on the statistical attributes of their occurrence frequencies. The SMOTE algorithm is used to balance the classes in terms of pattern counts. The classifier design is based on majority voting ensemble with base classifiers including naïve Bayes, support vector machine, PART, decision tree and random forest. The study considers both binary and multi class problems. In the case of binary class problem, the two classes are the “normal” and the “attack” where the latter is formed by merging all of the trace data for all six different attack signatures into a single one. In the multiclass case, normal and six attack classes are each considered separately. A simulation study was performed to evaluate the performance of the proposed host-based misuse detection system. Multiple performance indicators and metrics including confusion matrix, true positive/negative, and false positive/negative rates were recorded. The proposed host-based misuse detection system demonstrated very high performance for detecting the attacks for the binary class problem although its performance for the multi-class case will benefit from further improvement.

Improved Perioperative Care of Elective Joint Replacement Patients: The Impact of an Orthopedic Perioperative Hospitalist

BackgroundWe developed an orthopedic hospitalist fellowship program for our total joint replacement program at a large urban academic medical center. The goal of the program was to improve patient outcomes, quality, and healthcare value through collaborative perioperative care and improved care coordination. This study evaluates the implementation and impact of our modified Hospitalist-Orthopaedic Team Co-management model on quality and performance metrics. MethodsWe reviewed our Quality Institute data using 3 databases for the 16 months before (PreOH) and 18 months after (PostOH) implementation. Procedural volume was identical during period 1 (1100 cases) vs period 2 (1119 cases). Metrics included mean LOS (length of stay), % patients discharged home, mean observed and expected LOS and LOS index, LOS variance, % ICU (intensive care unit) admissions, mean ICU days, % cases with complications, % mortality, 30-day readmission rate, and Hospital Consumer Assessment of Healthcare Providers and Systems scores. Statistical analysis was performed using the software imbedded in the database software. ResultsStatistically significant improvements occurred in multiple performance and quality metrics including mean hospital LOS for total knee replacement, percentage of total knee replacement patients discharged home, and percentage of patients discharged home for primary total hip arthroplasty, complication rate, and 30-day readmission rate. Reductions in % ICU admission and ICU LOS were seen but not statistically significant. HCAPHS scores improved in 6 of 8 categories, and was statistically significant in 3 of 8. ConclusionThe results of this study demonstrate that the modified Hospitalist-Orthopaedic Team Co-management model described above improves quality, cost effectiveness, and value for elective total joint replacement patients in comparison to the traditional consultation only model.

Predicted impacts of climate warming on aerobic performance and upper thermal tolerance of six tropical freshwater fishes spanning three continents.

Equatorial fishes, and the critically important fisheries based on them, are thought to be at-risk from climate warming because the fishes have evolved in a relatively aseasonal environment and possess narrow thermal tolerance windows that are close to upper thermal limits. We assessed survival, growth, aerobic performance and critical thermal maxima (CTmax) following acute and 21 d exposures to temperatures up to 4°C higher than current maxima for six species of freshwater fishes indigenous to tropical countries and of importance for human consumption. All six species showed 1.3–1.7°C increases in CTmax with a 4°C rise in acclimation temperature, values which match up well with fishes from other climatic regions, and five species had survival >87% at all temperatures over the treatment period. Specific growth rates varied among and within each species in response to temperature treatments. For all species, the response of resting metabolic rate (RMR) was consistently more dynamic than for maximum metabolic rate, but in general both acute temperature exposure and thermal acclimation had only modest effects on aerobic scope (AS). However, RMR increased after warm acclimation in 5 of 6 species, suggesting incomplete metabolic compensation. Taken in total, our results show that each species had some ability to perform at temperatures up to 4°C above current maxima, yet also displayed certain areas of concern for their long-term welfare. We therefore suggest caution against the overly broad generalization that all tropical freshwater fish species will face severe challenges from warming temperatures in the coming decades and that future vulnerability assessments should integrate multiple performance metrics as opposed to relying on a single response metric. Given the societal significance of inland fisheries in many parts of the tropics, our results clearly demonstrate the need for more species-specific studies of adaptive capacity to climate change-related challenges.

Universal and Convenient Optimization Strategies for Three-Terminal Memristors

Neuromorphic computing, i.e., brainlike computing, has attracted a great deal of attention because of its exceptional performance. For the hardware implementation of neuromorphic systems, the desired key building blocks, artificial synapses, have been intensively investigated recently. However, many issues, such as the small state number, low reliability, and high energy consumption, have complicated the path to real applications. Therefore, methods that can improve the performance of the artificial synapses are highly desired. Although different artificial synapses have diverse working mechanisms, universal optimization strategies that can be applied to most three-terminal field-effect-transistor-type artificial synapses are proposed in this paper. Instead of wasting the third terminal in the device structure, the working condition can be effectively tuned by this third terminal. The key parameters, such as the gate electric field intensity and distribution, can be adjusted, and the performance is thereby tuned. In this manner, multiple performance metrics are optimized, such as the current change per pulse ( $\Delta \text{I}$ ), the linearity, the uniformity, and the power consumption. The mechanisms behind these strategies are also investigated to strengthen the effectiveness. This paper will push the performance of the current artificial synapses to a new level.

Time-optimized management of IoT nodes

The vision of Internet of Things (IoT) aims to offer a vast infrastructure of numerous interconnected devices usually called IoT nodes. The infrastructure consists of the basis of pervasive computing applications. Applications can be built with the participation of the IoT nodes that interact in very dynamic environments. In this setting, one can identify the need for applying updates in the software/firmware of the autonomous nodes. Updates may include software extensions and patches significant for the efficient functioning of the IoT nodes. Legacy methodologies involve centralized models where complex algorithms and protocols are adopted for the distribution of the updates to the nodes. This paper proposes a distributed approach where each node is responsible to initiate and conclude the update process. We envision that each node monitors specific performance metrics (related to the node itself and/or the network) and based on a time-optimized scheme identifies the appropriate time to perform the update process.We propose the adoption of a finite horizon optimal stopping scheme. Our stopping model originates in the Optimal Stopping Theory (OST) and takes into account multiple performance metrics. The aim is to have the nodes capable of identifying when their performance and the performance of the network are of high quality. In that time, nodes could be able to efficiently conclude the update process. We provide a set of formulations and the analysis of our problem. Extensive experiments and a comparison assessment reveal the advantages of the proposed solution.

A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms

Evolutionary algorithms are optimization methods commonly used to solve engineering and business optimization problems. The parameters in evolutionary algorithm must be perfectly tuned in a way that the optimization algorithm solves the optimization problems efficiently and effectively. Several parameter tuning approaches with a single performance metric have been proposed in the literature. However, simultaneous consideration of multiple performance metrics could provide the optimal setting for the parameters in the evolutionary algorithm. In this research, a new hybrid parameter tuning approach is proposed to simultaneously optimize the performance metrics of the evolutionary optimization algorithm while it is used in solving an optimization problem. The proposed hybrid approach provides the optimal value of parameters of the evolutionary optimization algorithm. The proposed approach is the first parameter tuning approach in the evolutionary optimization algorithm which simultaneously optimizes all performance metrics of the evolutionary optimization algorithm. To do this, a full factorial design of experiment is used to find the significant parameters of the evolutionary optimization algorithm, as well as an approximate equation for each performance metric. The individual and composite desirability function approaches are then proposed to provide the optimal setting for the parameters of the evolutionary optimization algorithm. For the first time, we use the desirability function approach to find an optimal level for the parameters in the evolutionary optimization algorithm. To show the real application of the proposed parameter tuning approach, we consider two multi-objective evolutionary algorithms, i.e., a multi-objective particle swarm optimization algorithm (MOPSO) and a fast non-dominated sorting genetic algorithm (NSGA-III) and solve a single machine scheduling problem. We demonstrate the applicability and efficiency of the proposed hybrid approach in providing the optimal values of all parameters of the evolutionary optimization algorithms to optimize their performance in solving an optimization problem.

HypAp: A Hypervolume-Based Approach for Refining the Design of Embedded Systems

Designing complex embedded systems requires simultaneous optimization of multiple system performance metrics that can be addressed by applying Pareto-based multiobjective optimization techniques. At the end of this type of optimization process, designers always face Pareto fronts (PFs) including a large number of near-optimal solutions from which selecting the most proper system implementation is potentially infeasible. In this letter, for the first time, we present HypAp, a hypervolume-based automated approach to systematically help designers efficiently choose their preferred solutions after the optimization process. HypAp is a two-stage approach relying on clustering Pareto optimal solutions and then finding a subset of solutions that maximizes the hypervolume by using a genetic algorithm. The performance of HypAp is evaluated through applying HypAp to the PF by the case study of mapping applications on network-on-chip-based heterogeneous MPSoC.