Objective Function Evaluations Research Articles

The Relationship between Depression Severity and Prefrontal Hemodynamic Changes in Adolescents with Major Depression Disorder: A Functional Near-infrared Spectroscopy Study.

: Numerous studies have identified hemodynamic changes in adults with major depressive disorder (MDD) by using functional near-infrared spectroscopy (fNIRS). However, studies on adolescents with MDD are limited. As adolescence is a stage of rapid brain development, differences may occur depending on age. This study used fNIRS as an objective tool to investigate hemodynamic changes in the frontal lobe according to depression severity and age in adolescents with MDD. : Thirty adolescents (12 aged 12-15 years and 18 aged 16-18 years) were retrospectively investigated. The Children's Depression Inventory was used as a psychiatric evaluation scale, fNIRS was used as an objective brain function evaluation tool, and the Verbal Fluency Test was performed. : During the Verbal Fluency Test, in the younger MDD group, oxygenated-hemoglobin concentration increased in the right dorsolateral prefrontal cortex region as the severity of depression increased. In the older MDD group, the oxygenated-hemoglobin concentration decreased in the right dorsolateral prefrontal cortex region as the severity of depression increased. : These results suggest that fNIRS may be an objective tool for identifying age differences among adolescents with MDD. To generalize the results and verify fNIRS as a potential biomarker tool, follow-up studies with a larger sample group should be conducted.

Managing interruptions in appointment schedules via patient notification

Interruptions in appointment schedules are common in practice, yet many clinics use generic appointment scheduling templates and sub-optimal interruption management strategies that fail to address dynamics aspects of daily healthcare operations. In this study, we consider patient no-shows, patient punctuality, service time variability, random walk-in patient arrivals and other common appointment interruptions to determine optimal single-server, intra-day appointment schedules. Using a modified depth first search (DFS) algorithm that relies on a sample average approximation (SAA) technique for objective function evaluation, we minimize a weighted sum of expected waiting times for scheduled and walk-in patients, physician idle time, and physician overtime. Extensive experimentation shows the proposed algorithm solves realistic problems in reasonable time. Furthermore, we propose a sequential notification procedure (SNP) that serves as a dynamic correcting mechanism to address infrequent and unexpected interruptions with a relatively long duration. Considering possible patient responses to notification, SNP notifies scheduled patients in advance to prevent excessive patient waiting and physician overtime. Furthermore, we propose an easy-to-use patient notification heuristic (NH) that can be adopted by clinics using traditional appointment systems. Experimentation shows that both SNP and NH provide significant value, improving operational outcomes. We demonstrate the value of patient notification increases as interruption levels increase and when patients are responsive to notifications.

FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks

The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optimal designs involves evaluating designs in an iterative process, and the measurements are used to evaluate an acquisition function that guides the search process. However, measuring different objectives incurs different costs. For example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not consider this difference in objective evaluation cost, potentially incurring expensive evaluations of objective functions in the optimization process. In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, which we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. For evaluating each design, FlexiBO selects the objective with higher relative gain by weighting the improvement of the hypervolume of the Pareto region with the measurement cost of each objective. This strategy, therefore, balances the expense of collecting new information with the knowledge gained through objective evaluations, preventing FlexiBO from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than the best method in state-of-the-art multi-objective optimization.

EMEF: Ensemble Multi-Exposure Image Fusion

Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don’t utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-train an imitator network and tune the imitator in the runtime. In the first stage, we make a unified network imitate different MEF targets in a style modulation way. In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair. In the experiment, we construct EMEF from four state-of-the-art MEF methods and then make comparisons with the individuals and several other competitive methods on the latest released MEF benchmark dataset. The promising experimental results demonstrate that our ensemble framework can “get the best of all worlds”. The code is available at https://github.com/medalwill/EMEF.

Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems.

Method for Parameter Tuning of Hybrid Optimization Algorithms for Problems with High Computational Costs of Objective Function Evaluations

In this paper, the authors focus on presenting the methodology for tuning optimization algorithm parameters, with a special focus on evolutionary algorithm applications. The problem considered concerns the phenomenon of nonlinear buckling of the automotive shock absorber, which itself is solved using a commercial application of the finite element method (FEM) simulation. These analyses are usually time-consuming; therefore, the authors decided to use a surrogate model, which mimics the behavior of the actual nonlinear FEM simulation. Surrogate modeling (metamodeling) is utilized to drastically shorten the simulation time, and thus study numerous algorithm parameter combinations, allowing for tuning them and providing a robust and efficient tool for optimization. The example shown in this paper is related to the minimization of the shock absorber weight, taking into account the stability of the system. The presented method can be used in any optimization problem where the high computational cost of objective function evaluations prevents tuning of the algorithm parameters.

Real-Time Hybrid Test Control Research Based on Improved Electro-Hydraulic Servo Displacement Algorithm

Real-time hybrid testing (RTH) is a test method for dynamic loading performance evaluation of structures, which is divided into digital simulation and physical testing, but the integration of the two may lead to problems such as time lag, large errors, and slow response time. The electro-hydraulic servo displacement system, as the transmission system of the physical test structure, directly affects the operational performance of RTH. Improving the performance of the electro-hydraulic servo displacement control system has become the key to solving the problem of RTH. In this paper, the FF-PSO-PID algorithm is proposed to control the electro-hydraulic servo system in real-time hybrid testing (RTH), which uses the PSO algorithm to operate the optimized PID parameters and the feed-forward compensation algorithm to compensate the displacement. First, the mathematical model of the electro-hydraulic displacement servo system in RTH is presented and the actual parameters are determined. Then, the objective evaluation function of the PSO algorithm is proposed to optimize the PID parameters in the context of RTH operation, and a displacement feed-forward compensation algorithm is added for theoretical study. To verify the effectiveness of the method, joint simulations were performed in Matlab/Simulink to compare and test FF-PSO-PID, PSO-PID, and conventional PID (PID) under different input signals. The results show that the proposed FF-PSO-PID algorithm effectively improves the accuracy and response speed of the electro-hydraulic servo displacement system and solves the problems of RTH time lag, large error, and slow response.

Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

Functional outcomes after transanal ileal pouch-anal anastomosis for ulcerative colitis: narrative review of the current literature.

The transanal approach to ileal pouch-anal anastomosis (Ta-IPAA) for ulcerative colitis was introduced in 2015 and has since been shown to be a safe and feasible technique, although the impact of this approach on patient function remains unclear. A systematic literature review was performed to identify studies exploring functional outcomes and quality of life after Ta-IPAA. Seven papers were identified, which all demonstrated satisfactory functional outcomes after Ta-IPAA as measured by tools such as Cleveland Global Quality of Life (CGQOL), Oresland Score (OS), Pouch Functional Score (PFS), and Inflammatory Bowel Disease Questionnaire (IBDQ). Many gaps in the literature were identified including paucity of follow-up data beyond the 1-year mark, lack of fertility and fecundity assessment as functional outcomes, and limited evaluation of objective physiologic anal sphincter function. The Ta-IPAA therefore shows promise for good functional results in the short-term, although further research will be required to evaluate the stability of function over time as the technique becomes increasingly integrated into modern surgical practice.

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov–Smirnov, Mann–Whitney, and Kruskal–Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

Performance-based optimization of nonlinear Friction-Folded PTMDs of structures subjected to stochastic excitation

Alternative Tuned Mass Damper (TMD) configurations have been proposed in the literature for performance improvement and maintenance, mainly modifying their restoring force or dissipation mechanisms. In this context, Folded-Pendulum TMDs (FPTMDs) are appealing for tuning low-frequency structures because they are compact and easy to retune while avoiding sliding mechanisms. In turn, Friction Dampers (FDs) are equally attractive due to their minimal maintenance and easy desired capacity adjustment. Nevertheless, despite recent advances in integrating FDs with PTMDs, a comprehensive literature survey reveals that Reliability-Based Design Optimization (RBDO) studies for PTMDs or Folded-PTMDs with FDs cannot be found to the best of the authors’ knowledge. Hence, the present paper aims at performing an original RBDO of single and multiple Friction-Folded-PTMDs in buildings subjected to stochastic excitation. Unlike the existing optimization studies in this field, a complete nonlinear description (dry friction plus large rotations) is employed because the considered applications involve high-intensity excitations, like wind and seismic loading. Finally, scenarios with multiple absorbers are included due to their associated performance and constructional advantages. Optimal design of multiple Folded-PTMDs with FDs has not yet been addressed, as they increase the optimization problem complexity leading to multimodal and convex objective functions. Accordingly, an active-learning Kriging-based Efficient Global Optimization (EGO) procedure is used, which allows for finding the optimum solutions with only a few objective function evaluations. The application cases to two distinct buildings subject to wind and seismic loadings show that using Friction-FPTMDs allows for obtaining a control strategy with an appropriate performance while ensuring practical advantages over classical TMDs.

A novel feature selection method considering feature interaction in neighborhood rough set

Feature selection has been shown to be a highly valuable strategy in data mining, pattern recognition, and machine learning. However, the majority of proposed feature selection methods do not account for feature interaction while calculating feature correlations. Interactive features are those features that have less individual relevance with the class, but can provide more joint information for the class when combined with other features. Inspired by it, a novel feature selection algorithm considering feature relevance, redundancy, and interaction in neighborhood rough set is proposed. First of all, a new method of information measurement called neighborhood symmetric uncertainty is proposed, to measure what proportion data a feature contains regarding category label. Afterwards, a new objective evaluation function of the interactive selection is developed. Then a novel feature selection algorithm named (NSUNCMI) based on measuring feature correlation, redundancy and interactivity is proposed. The results on the nine universe datasets and five representative feature selection algorithms indicate that NSUNCMI reduces the dimensionality of feature space efficiently and offers the best average classification accuracy.

Anisotropy-Based Adaptive Polynomial Chaos Method for Hybrid Uncertainty Quantification and Reliability-Based Design Optimization of Structural-Acoustic System

The evaluation of objective functions and component reliability in the optimisation of structural-acoustic systems with random and interval variables is computationally expensive, especially when strong nonlinearity exhibits between the response and input variables. To reduce the computational cost and improve the computational efficiency, a novel anisotropy-based adaptive polynomial chaos (ABAPC) expansion method was developed in this study. In ABAPC, the anisotropy-based polynomial chaos expansion, namely the retained order of polynomial chaos expansion (PCE) differs from each variable, is used to construct the initial surrogate model instead of first-order polynomial chaos expansion in conventional methods. Then, an anisotropy-based adaptive basis growth strategy was developed to reduce the estimation of the coefficients of the polynomial chaos expansion method and increase its computational efficiency. Finally, to solve problems with probabilistic and interval parameters, an adaptive basis truncation strategy was introduced and implemented. Using the ABAPC method, the computational cost of reliability-based design optimisation for structural-acoustic systems can be efficiently reduced. The effectiveness of the proposed method were demonstrated by solving two numerical examples and optimisation problems of a structural-acoustic system.

Matrix for Mucosal Regeneration in Transoral Glossectomy for Squamous Cell Carcinoma: Objective and Subjective Functional Evaluation.

Numerous options to manage local reconstruction following transoral partial glossectomy are possible. In this work, we present our experience using a matrix for mucosal regeneration, Integra®, after transoral resections of squamous cell carcinoma of the oral tongue. A retrospective analysis of patients treated for tongue carcinoma and reconstruction with Integra®, from September 2017 to September 2022. Functional outcomes were evaluated by measuring swallowing and speech abilities, tongue motility, and subjective quality of life. The series accounts for 13 consecutive patients, staged from Tis to T3, no positive resection margins were found, average defect size was 17.8 cm2. The average histologically measured depth of invasion was 4.1 mm (range 2-12 mm), and no recurrences were observed during follow-up. All patients maintained excellent swallowing function, the average number of recognized words by an external listener during a phone call was 70.5 out of 75, the lingual motility test was good (a mean score of 4.5 out of 6 movements correctly executed) and subjective questionnaires results were optimal. Less satisfying functional results were recorded in elderly patients receiving a wider surgical resection. This reconstructive technique for allows obtaining optimal healing and functional outcomes in patients with tumors suitable for transoral glossectomy.

The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case

Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation into account are presented in the framework of a deterministic trust-region method. The results show that the presence of intrinsic noise may dominate these bounds, in contrast with what is known for methods in which the inexactness in function and derivatives’ evaluations is fully controllable. Moreover, the new analysis provides estimates of the optimality level achievable, should noise cause early termination. Numerical experiments are reported that support the theory. The analysis finally sheds some light on the impact of inexact computer arithmetic on evaluation complexity.

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov–Smirnov, Wilcoxon, and Kruskal–Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.

Hydrodynamic optimization of pump-turbine runner

The paper suggests the approach to automatic CFD-based optimization of the pump-turbine runner. An in-house 3D flow solver CADRUN is used for the solution of steady-state RANS equations and evaluation of objective functions in representative operating points of turbine and pump operating regimes. Both efficiency and cavitation characteristics are taken into account. Two approaches have been considered, resulting in 4- and 3-objective shape optimization problems. These problems were solved using multi-objective genetic algorithm. It was shown that the obtained optimized runners feature increased efficiency both in turbine and pump mode with cavitation performance, similar to that of the initial runner.

Using physical property surrogate models to perform accelerated multi-fidelity optimization of force field parameters.

Accurate representations of van der Waals dispersion-repulsion interactions play an important role in high-quality molecular dynamics simulations. Training the force field parameters used in the Lennard Jones (LJ) potential typically used to represent these interactions is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The large computational expense of these simulations, especially when many parameters must be trained simultaneously, limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for more global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This approach allows for fast evaluation of approximate objective functions, greatly accelerating searches over parameter space and enabling the use of optimization algorithms capable of searching more globally. In this study, we use an iterative framework which performs global optimization with differential evolution at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 (Parsley) force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. Additionally, this technique often finds significantly different parameter minima that have comparably accurate performance. In most cases, these parameter sets are transferable to other similar molecules in a test set. Our multi-fidelity technique provides a platform for rapid, more global optimization of molecular models against physical properties, as well as a number of opportunities for further refinement of the technique.

Objective evaluation of thyroid function in postmenopausal women

Background: Thyroid dysfunction is more prevalent among elderly female. Assessment of thyroid dysfunction in the postmenopausal women is difficult due to their age, associated comorbidities, and ongoing treatment. Aims and Objectives: The aim of the study was to evaluate the thyroid status, presence of relative estrogen dominance and correlation of thyroid status with reproductive hormones in postmenopausal women. Materials and Methods: This cross-section study consists 40 postmenopausal women between age group 45 and 55 years. All participants were subjected to measure serum thyroid stimulating hormone (TSH) levels. Based on TSH levels, subjects were divided into normal thyroid function group (n = 12) and abnormal thyroid function group (n = 12). Serum was assayed for thyroxin binding globin (TBG), thyroid peroxidase antibody (TPO), estrogen, progesterone, and progesterone to estrogen ratio (P: E) in subjects of both the groups. Results: Around, 12 (30%) postmenopausal women had TSH values outside the range of 0.3–6.5 μIU/mL. TPO was positive only in one subject and 12.5% showed elevated TBG levels in abnormal thyroid group. Mean estrogen levels were significantly high, mean progesterone levels were significantly low and P: E was low in abnormal thyroid level group. A significant difference (P < 0.05) was observed on comparing the mean values of serum estrogen, progesterone, and P: E ratio among the groups. A significant positive correlation between TSH and serum estrogen, progesterone, and P: E and inverse correlation between estrogen and P: E was observed in abnormal thyroid level group. Conclusion: The thyroid dysfunction in postmenopausal women is associated with estrogen dominance. Therefore, menopausal women should be routinely screened for thyroid dysfunction, as part of their annual examination.

Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.