Objective Function Evaluations Research Articles

ESTUDIO COMPARATIVO DE ALGORITMOS DE OPTIMIZACIÓN CON OBJETIVOS MÚLTIPLES PARA UN MODELO DE AUTÓMATA CELULAR

Cellular Automata (CA) models can represent dynamic systems which are discrete in space and time that reflects the effect of intrinsic parameters where individual events are considered to occur from randomness. A CA model of two agents' chemical kinetics has been optimized earlier using NSGA-II based on Evolutionary Algorithm (EA). But the stochastic nature of the CA model along with its high sensitivity on the model parameters requires extensive investigation using different optimization algorithms. For this purpose, in the current study, four more recently developed and popular optimization algorithms based on EA, called NSGA-IIr, NSGA-IIa, AbYSS and MOEA/D, have been considered for investigation based on various performance measuring parameters. The study also compares the performances of the algorithms for different computational efforts with an objective to minimize the required number of objective function evaluations. Simulation results and Friedman rank statistical test show NSGA-IIa and NSGA-IIr as the best choices to optimize the CA stochastic model across any number of objective function evaluations. Though the choice of optimization algorithm does not change with function evaluations, higher function evaluations improve the pseudo-pareto front for the CA optimization problem. Such results will facilitate the use of stochastic CA models to represent complex (bio)-chemical networks.

Distributed Pareto Optimization for Large-Scale Noisy Subset Selection

Subset selection, aiming to select the best subset from a ground set with respect to some objective function, is a fundamental problem with applications in many areas, such as combinatorial optimization, machine learning, data mining, computer vision, information retrieval, etc. Along with the development of data collection and storage, the size of the ground set grows larger. Furthermore, in many subset selection applications, the objective function evaluation is subject to noise. We thus study the large-scale noisy subset selection problem in this paper. The recently proposed DPOSS algorithm based on multiobjective evolutionary optimization is a powerful distributed solver for large-scale subset selection. Its performance, however, has been only validated in the noise-free environment. In this paper, we first prove its approximation guarantee under two common noise models, i.e., multiplicative noise and additive noise, disclosing that the presence of noise degrades the performance of DPOSS largely. Next, we propose a new distributed multiobjective evolutionary algorithm called DPONSS for large-scale noisy subset selection. We prove that the approximation guarantee of DPONSS under noise is significantly better than that of DPOSS. We also conduct experiments on the application of sparse regression, where the objective evaluation is often estimated using a sample data, bringing noise. The results on various real-world data sets, whose size can reach millions, clearly show the excellent performance of DPONSS.

Validation of computerized square-drawing based evaluation of motor function in patients with stroke

Human-administered clinical scales are commonly used for quantifying motor performance and determining the course of therapy in post-stroke individuals. Computerized methods aim to improve consistency, resolution and duration of patients’ evaluation. The objective of this study was to test the validity of computerized square-drawing test (DT) for assessment of shoulder and elbow function by using novel set of DT-based kinematic measures and explore their relation with Wolf Motor Function Test (WMFT) scoring. Forty-seven stroke survivors were tested before and after the rehabilitation program. DT involved drawing a square in horizontal plane using a mechanical manipulandum and a digitizing board. Depending on the initial classification of patients into low or high performance groups, the two different outcome metrics were derived from DT kinematic data for evaluation of each group. Linear regression models applied to map DT outcome values to WMFT scores for both groups resulted with high correlation coefficients and low mean absolute prediction error. In conclusion, we have identified a set of kinematic measures suitable for fast and objective motor function evaluation and functional classification, strongly correlating with WMFT score in post-stroke individuals. The results support validation of square-drawing motor function assessment, encouraging its use in clinical settings.

Efficient computation of expected hypervolume improvement using box decomposition algorithms

In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms. MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to decide the next evaluation site by maximizing or minimizing an infill criterion. A commonly used criterion in MOBGO is the Expected Hypervolume Improvement (EHVI), which shows a good performance on a wide range of problems, with respect to exploration and exploitation. However, so far, it has been a challenge to calculate exact EHVI values efficiently. This paper proposes an efficient algorithm for the exact calculation of the EHVI for in a generic case. This efficient algorithm is based on partitioning the integration volume into a set of axis-parallel slices. Theoretically, the upper bound time complexities can be improved from previously O (n^2) and O(n^3), for two- and three-objective problems respectively, to varTheta (nlog n), which is asymptotically optimal. This article generalizes the scheme in higher dimensional cases by utilizing a new hyperbox decomposition technique, which is proposed by Dächert et al. (Eur J Oper Res 260(3):841–855, 2017). It also utilizes a generalization of the multilayered integration scheme that scales linearly in the number of hyperboxes of the decomposition. The speed comparison shows that the proposed algorithm in this paper significantly reduces computation time. Finally, this decomposition technique is applied in the calculation of the Probability of Improvement (PoI).

Upper-limb actuated exoskeleton for muscular dystrophy patients: preliminary results.

Being able to perform a lost movement is an important experience towards increased independence and self-esteem, particularly for neuromuscular patients, who see their muscles weaken day after day. In this pilot study, preliminary results on the testing of a motorized upper-limb exoskeleton for muscular dystrophy patients are presented. The mechatronic system is a five Degrees of Freedom exoskeleton, which acts at shoulder, elbow, and wrist levels. It is designed to help severely impaired people to regain independence during daily-life activities. While wearing the exoskeleton, the user has the direct control of the system by actively piloting the position of end-effector by means of joystick or vocal control. The usability of the system and a quantitative assessment of arm functionality with and without the exoskeleton are evaluated on five muscular dystrophy patients. According to the objective functional benefit evaluation performed through the PUL scale, all participants strongly increased their range of motion and they were able to perform activities that were not possible without the exoskeleton, such as such as feeding, playing activities at the table, combing hair or using a keyboard. As for the evaluation of self-perceived functional benefit, four patients reflected the effective measured functional improvement. System usability has been evaluated to be good.

Application of non-animal-inspired evolutionary algorithms to reservoir operation: an overview.

Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

PF765 RESULTS OF AUTOLOGOUS STEM CELL TRANSPLANT (ASCT) IN PATIENTS ABOVE THE AGE OF 70 ARE COMPARABLE TO THE RESULTS IN YOUNGER PATIENTS

Background:High dose chemotherapy and autologous stem cell transplant is an important therapeutic procedure for select solid tumours and haematological malignancies providing chance of durable remission following induction therapy and in some patient potential cure. Increasing numbers of patients above the age of 70 are offered high dose chemotherapy and Auto‐PBSCT. This retrospective analysis compares the outcomes, morbidity and mortality of Auto‐PBSCT in patients above the age of 70 to those in the age group of 65‐69.Aims:To evaluate the results of ASCT in patients with lymphoma or myeloma who were above the age of 70 yrs to the reults in matched group in the age group of 65 to 70 yr. with the aim to compare engraftment data, morbidity, mortality and overall survival.Methods:Retrospective evaluation using case notes, electonic patient records and haematology database.Results:We analysed 54 patients, ages 70 and over, and 56 patients, ages between 65 and 69 years, who received AutoPSCT from 1990 to 2018 in Haematology and Transplant Unit, The Christie NHS Foundation Trust, Manchester, UK. Median age was 71yr(70–75) and 67yr(65–69). Male to female ratio are 38:16 (>70yr) and 41:15 (65–69). The two indications for transplants are Myeloma and lymphoma. The proportions are similar for both age groups, Myeloma:Lymphoma 37(68.5%):17(31.5%) in >70yrs and 40(71.4%):16(28.6%) in 65–69yr The numbers of patients undergoing second Auto‐PBSCT are 9(17%) in >70yr and 11(20%) in 65–69yrs. The follow up period was 2 years (43d‐14.1years) in >70yrs and 4 years (41d‐16.3years) in the 65–69yrs. Median time to neutrophil engraftment (ANC>0.5) was 12 days(9–19days) and 12 days(9–21days). Median platelet recovery (>25) was 21days(10–195days) and 29 days (10–694 days). Median hospitalisation was 27 days (14–91days) and 21days (14–60days). In terms of morbidity during transplant, patients needing intensive or high dependency care was 8 (15%) in >70yr and 5(9%) in 65–69yrs). Patients requiring readmission post discharge was 14(26%) in >70yrs and 9 (16%) in younger age group.here was no difference in treatment related mortality and less than 3% in both groups.Summary/Conclusion:It is feasible to offer ASCT to patients above the age of 70 yr. Patient selection is crucial and should be based not only on the objective organ function evaluation but also the subjective assessment of transplant physician about appropriateness to undergo the ASCT therapy. The decision should be based on the individualised risk benefit ratio, risk of morbidity and expected survival benefit.image

Regression analysis for detecting epileptic seizure with different feature extracting strategies.

Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem

Particle swarm optimization is a population-based stochastic algorithm designed to solve difficult optimization problems, such as the flexible job shop scheduling problem. This problem consists of scheduling a set of operations on a set of machines while minimizing a certain objective function. This paper presents a two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. The upper level handles the operations-to-machines mapping, while the lower level handles the ordering of operations on machines. A lower bound-checking strategy on the optimal objective function value is used to reduce the number of visited solutions and the number of objective function evaluations. The algorithm is benchmarked against existing state-of-the-art algorithms for the flexible job shop scheduling problem on a significant number of diverse benchmark problems.

Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation

This study proposes a method for optimally selecting the operating parameters of an energy storage system (ESS) for frequency regulation (FR) in an electric power system. First, the method allows the optimal objective function of the selected parameters to be set in a flexible manner according to the electric market environment. The objective functions are defined so that they could be used under a variety of electricity market conditions. Second, evaluation frequencies are created in order to simulate the overall lifespan of the FR-ESS. Third, calendar and cycle degradation models are applied to the battery degradation, and are incorporated into evaluations of the degradation progress during the entire FR-ESS lifespan to obtain more accurate results. A calendar life limit is set, and the limit is also considered in the objective function evaluations. Fourth, an optimal parameter calculation algorithm, which uses the branch-and-bound method, is proposed to calculate the optimal parameters. A case study analyzes the convergence of the proposed algorithm and the results of the algorithm under various conditions. The results confirmed that the proposed algorithm yields optimal parameters that are appropriate according to the objective function and lifespan conditions. We anticipate that the proposed FR-ESS algorithm will be beneficial in establishing optimal operating strategies.

Shape Optimization of Reentry Vehicles to Minimize Heat Loading

The objective of the current study is to design an optimum reentry vehicle shape that minimizes heat loading subject to constraints on the maximum values of surface heat flux and temperature. A new heat loading formulation is developed for objective function evaluations. Axisymmetric Navier–Stokes and finite-rate chemical reaction equations are solved to evaluate the objective and constraint functions. The Menter SST turbulence model is employed for turbulence closure. A gradient-based method is used for optimization. The sensitivities of the objective and constraint functions are evaluated using the finite-difference method. In shape optimization, the geometry change or the geometry itself is parameterized using different numbers of nonuniform rational basis spline (NURBS) or Bezier curves. Designs are performed at different trajectory points of the IRV-2 vehicle. The effects of flight path angle and reentry velocity on the heat transfer and trajectory characteristics of the original and designed geometries are quantified.

Interval Multiobjective Optimization With Memetic Algorithms.

One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Many global optimization problems arising naturally in science and engineering exhibit some form of intrinsic ill-posedness, such as multimodality and insensitivity. Severe ill-posedness precludes the use of standard regularization techniques and necessitates more specialized approaches, usually comprised of two separate stages – global phase, that determines the problem's modality and provides rough approximations of the solutions, and a local phase, which refines these approximations.In this work, we attempt to improve one of the most efficient currently known approaches - Hierarchic Memetic Strategy (HMS) - by incorporating the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) into its local phase. CMA-ES is a stochastic optimization algorithm that in some sense mimics the behavior of population-based evolutionary algorithms without explicitly evolving the population. This way, it avoids, to an extent, the associated cost of multiple evaluations of the objective function.We compare the performance of the HMS on relatively simple multimodal benchmark problems and on an engineering problem. To do so, we consider two configurations: the CMA-ES and the standard SEA (Simple Evolutionary Algorithm). The results demonstrate that the HMS with CMA-ES in the local phase requires less objective function evaluations to provide the same accuracy, making this approach more efficient than the standard SEA.

Handling expensive multi-objective optimization problems with a cluster-based neighborhood regression model

This paper gives attention to multi-objective optimization in scenarios where objective function evaluation is expensive, that is, expensive multi-objective optimization. We firstly propose a cluster-based neighborhood regression model, which incorporates the linear regression technique to predict the descent direction and generate new potential offspring. Combining this model with the classical decomposition-based multi-objective optimization framework, we propose an efficient and effective algorithm for tackling computationally expensive multi-objective optimization problems. As opposed to the conventional approach of replacing the original time-consuming objective functions with the approximated ones obtained by surrogate model, the proposed algorithm incorporates the proposed regression model to serve as an operator producing higher-quality offspring so that the algorithm requires fewer iterations to reach a given solution quality. The proposed algorithm is compared with several state-of-the-art surrogate-assisted algorithms on a variety of well-known benchmark problems. Empirical results demonstrate that the proposed algorithm outperforms or is competitive with other peer algorithms, and has the ability to keep a good trade-off between solution quality and running time within a fairly small number of function evaluations. In particular, our proposed algorithm shows obvious superiority in terms of the computational time used for the algorithm components, and can obtain acceptable solutions for expensive problems with high efficiency.

Prediction of Pareto Dominance Using an Attribute Tendency Model for Expensive Multi-Objective Optimization

This paper proposes a novel surrogate-assisted multi-objective evolutionary algorithm, MOEA-ATCM, to solve expensive or black-box multi-objective problems with small evaluation budgets. The proposed approach encompasses a state-of-the-art MOEA based on a nondominated sorting genetic algorithm assisted by multi-fidelity optimization methods. A high-fidelity attribute tendency (AT) surrogate model was used to construct a linear decision space by introducing the knowledge of the objective space. A coarse model (CM) based on the AT model and correlation analyses of the objective functions and decision attributes were used to predict the Pareto dominance for candidates in the new decision space constructed by the AT model. Two major roles of MOEA-ATCM were identified: (1) the development of a new multi-fidelity surrogate-model-based method to predict Pareto dominance in a decision space that was then applied to MOEA, which does not need to dynamically update surrogate models in the optimization process and (2) the development of a Pareto dominance prediction method to obtain good nondominated solutions of expensive or black box problems with relatively few objective function evaluations. The advantages of MOEA-ATCM were verified by mathematical benchmark problems and a real-world multi-objective parameter optimization problem.

Multiobjective optimization of 2DOF controller using Evolutionary and Swarm intelligence enhanced with TOPSIS

In this paper, Evolutionary (NSGA-II and NSGA-III) and Swarm Intelligence (MOPSO) based algorithms enhanced with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to optimize five parameters of Two Degree Of Freedom (2DOF) controller. Three objective functions, one for set point tracking and two for disturbance rejections (flow variation of input fluid and temperature variation of input fluid both are in conflict) are deployed for the problem of shell and tube heat exchanger. Three test criteria IAE, ISE and ITAE function of error (set point tracking and disturbance rejection) and time are used for evaluation of objective functions. The Pareto set of solutions are obtained after optimizing all the five parameters of 2DOF controller. In order to obtain the comparative analysis of optimization algorithms (NSGA-II, NSGA-III, and MOPSO) all the Pareto optimal solutions are combined under three separate evaluation criteria IAE, ISE, and ITAE. TOPSIS a multiple criteria decision making method is used to rank the set of Pareto optimal solutions for reducing number of Pareto optimal solutions to a single solution. The best rank solution obtain for 2DOF controller parameters after applying TOPSIS on set of Pareto optimal solutions using Evolutionary (NSGA-II and NSGA-III) algorithms are compared with Swarm Intelligence (MOPSO) algorithm. To evaluate the performance optimization of 2DOF controller tuning, we compared the values of peak overshoot of step response, set point tracking error, disturbance rejection (both flow and temperature), settling time, and the percentage of solutions obtained from optimization algorithms under all three evaluation criteria IAE, ISE, and ITAE. MATLAB software tool is used to implement the above algorithms.

Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit

A method of fast multi-objective optimization and decision-making support for building retrofit planning is developed, and lifecycle cost analysis method taking into account of future climate condition is used in evaluating the retrofit performance. In order to resolve the optimization problem in a fast manner with recourse to non-dominate sorting differential evolution algorithm, the simplified hourly dynamic simulation modeling tool SimBldPy is used as the simulator for objective function evaluation. Moreover, the generated non-dominated solutions are treated and rendered by a layered scheme using agglomerative hierarchical clustering technique to make it more intuitive and sense making during the decision-making process as well as to be better presented.The suggested optimization method is implemented to the retrofit planning of a campus building in UPenn with various energy conservation measures (ECM) and costs, and more than one thousand Pareto fronts are obtained and being analyzed according to the proposed decision-making framework. Twenty ECM combinations are eventually selected from all generated Pareto fronts. It is manifested that the developed decision-making support scheme shows robustness in dealing with retrofit optimization problem and is able to provide support for brainstorming and enumerating various possibilities during the decision-making process.

Investigating surrogate-assisted cooperative coevolution for large-Scale global optimization

Problems involving large-scale global optimization (LSGO) are becoming more and more frequent. For this reason, the last few years have seen an increasing number of researchers interested in improving optimization metaheuristics in such a way as to cope effectively with high-dimensional search domains. Among the techniques to enhance scalability, one of the most studied is Cooperative Coevolution (CC), an effective divide-and-conquer strategy for decomposing a large-scale problem into lower-dimensional subcomponents. However, despite the progress made in the LSGO field, one of such optimizations can still require a very high number of objective function evaluations. Therefore, when the evaluation of a candidate solution requires complex calculations, LSGO can become a challenging task. Nonetheless, to date few studies have investigated the application of optimization metaheuristics to objective functions that are simultaneously high-dimensional and computationally significant. To address such a research issue, this article investigates a surrogate-assisted CC (SACC) optimizer, in which fitness surrogates are exploited within the low-dimensional subcomponents resulting from the problem decomposition. The SACC algorithm is investigated on a rich test-bed composed of 1000-dimensional problems. According to the results, SACC is able to significantly boost the convergence of the CC optimizer, leading in many cases to a relevant computational gain.

An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

Gaussian Process Surrogate Models for the CMA Evolution Strategy

This article deals with Gaussian process surrogate models for the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)-several already existing and two by the authors recently proposed models are presented. The work discusses different variants of surrogate model exploitation and focuses on the benefits of employing the Gaussian process uncertainty prediction, especially during the selection of points for the evaluation with a surrogate model. The experimental part of the article thoroughly compares and evaluates the five presented Gaussian process surrogate and six other state-of-the-art optimizers on the COCO benchmarks. The algorithm presented in most detail, DTS-CMA-ES, which combines cheap surrogate-model predictions with the objective function evaluations in every iteration, is shown to approach the function optimum at least comparably fast and often faster than the state-of-the-art black-box optimizers for budgets of roughly 25-100 function evaluations per dimension, in 10- and less-dimensional spaces even for 25-250 evaluations per dimension.