Objective Function Evaluations Research Articles

An Effective Simulated Annealing for Influence Maximization Problem of Online Social Networks

The influence maximization problem (IMP) is one of the most well-known problems in the research domain of online social networks (OSN) that has attracted the attention of many researchers from different disciplines in recent years. One of the reasons is that the speed of information propagation in the OSN can be increased if we can find out users that have maximum influence on other users. The traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although metaheuristic algorithms can be used to solve the IMP, there is still plenty of room for improvement. That is why an effective and efficient algorithm is presented in this paper. The proposed algorithm, called simulated annealing with search partition (SASP), is based on a search space partitioning mechanism to enhance the search performance of simulated annealing for the IMP. The experimental results show that the proposed algorithm outperforms the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations.

Calibration of the demand structure for dynamic traffic assignment using flow and speed data: exploiting the advantage of distributed computing in derivative-free optimization algorithms

Abstract Stochastic optimization algorithms have been used in the recent literature as a preferred way for calibrating Dynamic Traffic Assignment (DTA) models, as the computation of explicit gradients is numerically too cumbersome on real networks. However, early experiences based on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm have shown performance issues when the number of variables becomes large. This suggests to focus on structural demand variables rather than to consider all components of origin-destination (O-D) matrices. Moreover, with the possibility of distributed computing, many algorithms that where not efficient in a standard configuration (i.e. sequential objective function evaluations within each iteration) can become a viable alternative to SPSA. For example, parallelization can be especially beneficial for genetic algorithms, which require a large number of independent function evaluations per iteration. In this paper we examine several optimization algorithms applied to dynamic demand calibration using flow and speed field measurements. The problem is to minimize the distance between results of a dynamic network loading and traffic data observed on road links. This approach is investigated in the context of laboratory experiments, where known O-D matrices are perturbed after its dynamic assignment on the network, to prove the effectiveness of the proposed methodology.

Optimization of axial enrichment distribution for BWR fuels using scoping libraries and block coordinate descent method

An optimization method has been developed to search for the optimal axial enrichment distribution in a fuel assembly for a boiling water reactor core. The optimization method features: (1) employing the block coordinate descent method to find the optimal solution in the space of enrichment difference parameters, (2) using scoping libraries to reduce the amount of CASMO-4 calculation, and (3) integrating a core critical constraint into the objective function that is used to quantify the quality of an axial enrichment design. The objective function consists of the weighted sum of core parameters such as shutdown margin and critical power ratio. The core parameters are evaluated by using SIMULATE-3, and the cross section data required for the SIMULATE-3 calculation are generated by using CASMO-4 and scoping libraries. The application of the method to a 4-segment fuel design (with the highest allowable segment enrichment relaxed to 5%) demonstrated that the method can obtain an axial enrichment design with improved thermal limit ratios and objective function value while satisfying the core design constraints and core critical requirement through the use of an objective function. The use of scoping libraries effectively reduced the number of CASMO-4 calculation, from 85 to 24, in the 4-segment optimization case. An exhausted search was performed to examine the capability of the method in finding the optimal solution for a 4-segment fuel design. The results show that the method found a solution very close to the optimum obtained by the exhausted search. The number of objective function evaluation (OFV) in the exhausted search is 8707, and the number of OFV in the search using the optimization method is 41. The reduction in OFV shows the efficiency of the optimization method. The optimization designs with more than 4 enriched axial segments were also performed to see if better objective function values can be achieved by having more axial segments.

On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments.

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.

Only severe malocclusion correlates with mastication deficiency

ObjectivesThe relation between level of dentofacial deformity and extent of masticatory deficiency was studied. DesignThree groups of human young adults were formed: (i) subjects needing orthodontics plus orthognathic surgery (SevDFD, n=18), (ii) subjects needing orthodontic treatment only (ModDFD, n=12), and (iii) subjects needing no treatment (NoDFD, n=12). For mastication tests, carrot boluses were collected at the deglutition time. Bolus particle size range was expressed as d50 value, which was compared with the Masticatory Normative Indicator (MNI). Index of treatment need (IOTN), global oral health assessment index (GOHAI) and chewing kinematic characteristics were also recorded. We used a general linear model univariate procedure followed by a Student-Newman-Keuls test. ResultsAll the SevDFD subjects showed impaired mastication with MNI above the normal limit (d50 mean=7.23mm). All the ModDFD subjects but one were below this limit (d50 mean=2.54mm), and so could adapt to a low level of masticatory impairment as also indicated by kinematics. IOTN indicated a treatment need for ModDFD (3.7±0.5) and SevDFD (4.3±0.6) groups, while GOHAI values were unsatisfactory only for SevDFD (42.6±9.2 vs. 55.3±1.9). ConclusionsOur findings emphasize the need for an objective evaluation of masticatory function to discern truly deficient mastication from mild impairment allowing satisfactory adaptation of the function. However, malocclusions are known to worsen with time justifying thus their corrections as early as possible.

One algorithm for branch and bound method for solving concave optimization problem

The article describes the algorithm for branch and bound method for solving the concave programming problem, which is based on the idea of similarity the necessary and sufficient conditions of optimum for the original problem and for a convex programming problem with another feasible set and reverse the sign of the objective function. To find the feasible set of the equivalent convex programming problem we construct an algorithm using the idea of the branch and bound method. We formulate various branching techniques and discusses the construction of the lower objective function evaluations for the node of the decision tree. The article discusses the results of experiments of this algorithm for some famous test problems of a particular form.

Self-perception versus professional assessment of functional outcome after ablative surgery in patients with oral cancer.

The extent of functional impairment after ablative surgery in the orofacial region may be directly reflected in a reduction in Quality of Life. This study intended to compare the patients' perception with an objective functional evaluation of the orofacial system in order to bilaterally distinguish direct influence factors. A total of 45 patients were included in this study and were asked to complete the European Organisation for Research and Treatment of Cancer Core Questionnaire (EORTC QLQ-30) and the Head and Neck 35 Module (H&N 35). Afterward one independent speech therapist evaluated the patients applying the Frenchay Dysarthria Assessment regarding four main categories: mouth opening, tongue motility, swallowing and intelligibility. Comparisons between groups were performed using Whitney-Mann U-Wilcoxon test and calculating Spearman's rho. Overall the professional assessments by the speech therapists revealed significantly higher scores regarding intelligibility, swallowing and mouth opening when compared to the patients' self-perception. Smaller tumor sizes, no bone resection and local reconstruction techniques led to significantly better functional outcomes, when assessed by speech therapists. Swallowing was perceived significantly better by patients in cases of local reconstruction. From the professionals' point of view differences were perceived in more items compared to the patients' self-assessments, who widely experienced a more severe functional impairment. Physicians should take this into account when discussing adverse therapy effects with the patients.

Correspondence-free pose estimation for 3D objects from noisy depth data

Estimating the pose of objects from depth data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from noisy 2.5D depth images obtained from a consumer depth sensor. Initialized with a coarsely accurate pose, the proposed approach applies a hypothesize-and-test scheme that combines stochastic optimization and graphics-based rendering to refine the supplied initial pose, so that it accurately accounts for a sensed depth image. Pose refinement employs particle swarm optimization to minimize an objective function that quantifies the misalignment between the acquired depth image and a rendered one that is synthesized from a hypothesized pose with the aid of an object mesh model. No explicit correspondences between the depth data and the model need to be established, whereas pose hypothesis rendering and objective function evaluation are efficiently performed on the GPU. Extensive experimental results demonstrate the superior performance of the proposed approach compared to the ICP algorithm, which is typically used for pose refinement in depth images. Furthermore, the experiments indicate the graceful degradation of its performance to limited computational resources and its robustness to noisy and reduced polygon count models, attesting its suitability for use with automatically scanned object models and common graphics hardware.

An object localization optimization technique in medical images using plant growth simulation algorithm.

The analysis of leukocyte images has drawn interest from fields of both medicine and computer vision for quite some time where different techniques have been applied to automate the process of manual analysis and classification of such images. Manual analysis of blood samples to identify leukocytes is time-consuming and susceptible to error due to the different morphological features of the cells. In this article, the nature-inspired plant growth simulation algorithm has been applied to optimize the image processing technique of object localization of medical images of leukocytes. This paper presents a random bionic algorithm for the automated detection of white blood cells embedded in cluttered smear and stained images of blood samples that uses a fitness function that matches the resemblances of the generated candidate solution to an actual leukocyte. The set of candidate solutions evolves via successive iterations as the proposed algorithm proceeds, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The higher precision and sensitivity of the proposed scheme from the existing methods is validated with the experimental results of blood cell images. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

An objective functional evaluation of the flexor carpi ulnaris set of triple tendon transfer in radial nerve palsy.

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Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g., stochastic optimization or sampling average approximation, fail to learn exploitable patterns from collected data and require a lot of objective function evaluations. There is a need for techniques that alleviate the high cost of information acquisition and select sequential simulations optimally. In the field of deterministic single-objective unconstrained global optimization, the Bayesian global optimization (BGO) approach has been relatively successful in addressing the information acquisition problem. BGO builds a probabilistic surrogate of the expensive objective function and uses it to define an information acquisition function (IAF) that quantifies the merit of making new objective evaluations. In this work, we reformulate the expected improvement (EI) IAF to filter out parametric and measurement uncertainties. We bypass the curse of dimensionality, since the method does not require learning the response surface as a function of the stochastic parameters, and we employ a fully Bayesian interpretation of Gaussian processes (GPs) by constructing a particle approximation of the posterior of its hyperparameters using adaptive Markov chain Monte Carlo (MCMC) to increase the methods robustness. Also, our approach quantifies the epistemic uncertainty on the location of the optimum and the optimal value as induced by the limited number of objective evaluations used in obtaining it. We verify and validate our approach by solving two synthetic optimization problems under uncertainty and demonstrate it by solving the oil-well placement problem (OWPP) with uncertainties in the permeability field and the oil price time series.

Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for $$p\ge 1$$pź1) and to assume Lipschitz continuity of the p-th derivative, then an $$\epsilon $$∈-approximate first-order critical point can be computed in at most $$O(\epsilon ^{-(p+1)/p})$$O(∈-(p+1)/p) evaluations of the problem's objective function and its derivatives. This generalizes and subsumes results known for $$p=1$$p=1 and $$p=2$$p=2.

Parkinsonian Balance Deficits Quantified Using a Game Industry Board and a Specific Battery of Four Paradigms.

This study describes a cost-effective screening protocol for parkinsonism based on combined objective and subjective monitoring of balance function. Objective evaluation of balance function was performed using a game industry balance board and an automated analyses of the dynamic of the center of pressure in time, frequency, and non-linear domains collected during short series of stand up tests with different modalities and severity of sensorial deprivation. The subjective measurement of balance function was performed using the Dizziness Handicap Inventory questionnaire. Principal component analyses on both objective and subjective measurements of balance function allowed to obtained a specificity and selectivity for parkinsonian patients (vs. healthy subjects) of 0.67 and 0.71 respectively. The findings are discussed regarding the relevance of cost-effective balance-based screening system as strategy to meet the needs of broader and earlier screening for parkinsonism in communities with limited access to healthcare.

Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems

Deterministic optimization algorithms are very attractive when the objective function is computationally expensive and therefore the statistical analysis of the optimization outcomes becomes too expensive. Among deterministic methods, deterministic particle swarm optimization (DPSO) has several attractive characteristics such as the simplicity of the heuristics, the ease of implementation, and its often fairly remarkable effectiveness. The performances of DPSO depend on four main setting parameters: the number of swarm particles, their initialization, the set of coefficients defining the swarm behavior, and (for box-constrained optimization) the method to handle the box constraints. Here, a parametric study of DPSO is presented, with application to simulation-based design in ship hydrodynamics. The objective is the identification of the most promising setup for both synchronous and asynchronous implementations of DPSO. The analysis is performed under the assumption of limited computational resources and large computational burden of the objective function evaluation. The analysis is conducted using 100 analytical test functions (with dimensionality from two to fifty) and three performance criteria, varying the swarm size, initialization, coefficients, and the method for the box constraints, resulting in more than 40,000 optimizations. The most promising setup is applied to the hull-form optimization of a high speed catamaran, for resistance reduction in calm water and at fixed speed, using a potential-flow solver.

Interval Simulated Annealing applied to Electrical Impedance Tomography image reconstruction with fast objective function evaluation

The Electrical Impedance Tomography (EIT) reconstruction problem can be solved as an optimization problem in which the discrepancy between a simulated impedance domain and the observed one is minimized. This optimization problem can be solved by a combination of Simulated Annealing (SA) for optimization and the Finite Element Method (FEM) for simulating the impedance domain. A new objective function based on the total least squares error minimization is proposed. This objective function is ill-conditioned with dense meshes. Two possibilities to overcome ill-conditioning are considered: combination with another objective function (Euclidean distance) and inclusion of a regularization term. To speed up the algorithm, results from previous iterations are used to improve the present iteration convergence, and a preconditioner is proposed. This new reconstruction approach is evaluated with experimental data and compared with previous approaches.

Electromechanical impedance–based damage characterization using spectral element method

The high-frequency nature of impedance-based structural health monitoring makes the utilization of impedance signature for model-based damage characterization a challenging problem. In this study, a novel damage characterization approach that utilizes impedance signature measured with a single piezoelectric wafer is developed. Length-varying spectral elements are introduced to minimize the total number of elements required to describe the system, along with the number of damage characterization parameters. Several objective function definitions are studied and their behaviour with respect to each damage parameter is investigated. It has been found that an objective function definition based on the frequency shift in impedance peaks is the most effective definition compared to root mean square deviation and correlation-based objective functions. A novel damage localization method, referred to as sine-fit localization method, is developed based on the underlying periodic behaviour of impedance peak shifts as a function of damage location. The sine-fit localization method is integrated with gradient descend method in a two-stage optimization algorithm for damage characterization. The developed algorithm is capable of solving the ill-posed problem of damage characterization with few iterations and small number of objective function evaluations, which makes it computationally very efficient.

Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an e-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.

Comparison of Newton-type and SCE optimisation algorithms for the calibration of conceptual hydrological models

Hydrological model calibration has benefited from improved optimisation algorithms and advances in computing power over the last few decades. Stochastic optimisation methods have received particular attention, the SCE search has emerged as one of the best performing global optimisation algorithms for a single objective function. However, the improved robustness of stochastic optimisation usually comes at a considerable computational cost as the number of calibrated parameters increases. This work revisits the use of modern gradient-based algorithms. We investigate the performance of quasi-Newton and Gauss–Newton algorithms using SIMHYD and FUSE-536 calibrated to three Australian catchments. Analysis of the objective function surfaces detects micro-scale roughness and parameter insensitivity in both SIMHYD and FUSE-536. The SCE search provides the most robust performance for calibrating SIMHYD, but, somewhat surprisingly, struggles in the case of FUSE-536. In terms of computational costs, the Newton-type algorithms require about 20 times fewer objective function evaluations than the SCE search for SIMHYD and 50 times fewer evaluations for FUSE-536. Considering the chance of converging to the global optimum and the computational cost, we suggest that modern Newton-type algorithms may be competitive with, or even outperform, the SCE search. Improvements in the robustness of Newton-type algorithms should further increase their competitiveness.

Sympathetic skin response following single and combined sound and electrical stimuli in young healthy subjects

Introduction. Sympathetic skin response (SSR) is applied in evaluation of dysfunctions in autonomic nervous system. Among others, electrical and sound stimuli are most frequently used to evoke SSR. Aim. The aim of this study was to determine if the bell ring stimulus with parameters different from standard sound stimulation evokes similar reactions in autonomic system as electrical stimulus with defined parameters. Material and methods. SSR parameters were recorded following simultaneous sound and electrical stimulation. Twenty young volunteers (aged 23 ± 2.1 years) were examined once with SSR and R‑R interval variation (RRIV) tests in order to confirm lack of functional changes in autonomic nervous system. Results. Values of mean amplitudes of SSR were always higher during recordings from upper limbs than the lower ones irrespective of the three types of applied stimuli. Mean values of latencies were comparable when SSR were induced with acoustic, electrical and both stimuli during recordings performed from upper and lower extremities. Bell ring stimulus influenced only mean values of SSR area recorded both from upper (p ≤ 0.011) and lower (p ≤ 0.023) extremities. Heart beats variability in RRIV recordings changed at 13.5% which is comparable to results obtained by other authors. Conclusions. Results indicate that the application of different modalities stimuli evokes SSR with comparable parameters. Each of them can be used for objective evaluation of the sympathetic nervous system function. Both SSR and RRIV tests evaluating the function of two effector types should be applied for the diagnosis of the probable dysautonomia in patients who show unclear clinical symptoms.

Engineering optimization by constrained differential evolution with nearest neighbor comparison

It has been proposed to utilize nearest neighbor comparison to reduce the number of function evaluations in unconstrained optimization. The nearest neighbor comparison omits the function evaluation of a point when the comparison can be judged by its nearest point in the search population. In this paper, a constrained differential evolution (DE) algorithm is proposed by combining the ε constrained method to handle constraints with the nearest neighbor comparison method. The algorithm is tested using five benchmark engineering design problems and the results indicate that the proposed DE algorithm is able to find good results in a much smaller number of objective function evaluations than conventional DE and it is competitive to other state-of-the-art DE variants.