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Stress‐constrained topology optimization requires techniques for handling thousands to millions of stress constraints. This work presents a comprehensive numerical study comparing local and global stress constraint strategies in topology optimization. Four local and four global solution strategies are presented and investigated. The local strategies are based on either the augmented Lagrangian or the pure exterior penalty method, whereas the global strategies are based on the P‐mean aggregation function. Extensive parametric studies are carried out on the L‐shaped design problem to identify the most promising parameters for each solution strategy. It is found that (1) the local strategies are less sensitive to the continuation procedure employed in standard density‐based topology optimization, allowing achievement of better quality results using less iterations when compared with the global strategies; (2) the global strategies become competitive when P values larger than 100 are employed, but for this to be possible a very slow continuation procedure should be used; (3) the local strategies based on the augmented Lagrangian method provide the best compromise between computational cost and performance, being able to achieve optimized topologies at the level of a pure P‐continuation global strategy with P=300, but using less iterations. [ABSTRACT FROM AUTHOR]

Multi-arm clinical trials are complex experiments which involve several objectives. The demand for unequal allocations in a multi-treatment context is growing and adaptive designs are being increasingly used in several areas of medical research. For uncensored and censored exponential responses, we propose a constrained optimization approach in order to derive the design maximizing the power of the multivariate test of homogeneity, under a suitable ethical constraint. In the absence of censoring, we obtain a very simple closed-form solution that dominates the balanced design in terms of power and ethics. Our suggestion can also accommodate delayed responses and staggered entries, and can be implemented via response adaptive rules. While other targets proposed in the literature could present an unethical behavior, the suggested optimal allocation is frequently unbalanced by assigning more patients to the best treatment, both in the absence and presence of censoring. We evaluate the operating characteristics of our proposal theoretically and by simulations, also redesigning a real lung cancer trial, showing that the constrained optimal target guarantees very good performances in terms of ethical demands, power and estimation precision. Therefore, it is a valid and useful tool in designing clinical trials, especially oncological trials and clinical experiments for grave and novel infectious diseases, where the ethical concern is of primary importance. [ABSTRACT FROM AUTHOR]

Optimization problems have numerous real-life applications in science and engineering. The engineering design problems are usually subject to various constraints. Although many state-of-the-art metaheuristic optimization algorithms have been developed during the last decades, these algorithms require additional constraint-handling mechanisms to cope with constrained optimization problems. Therefore, selecting a suitable constraint-handling mechanism requires extensive trial-and-error experiments, which is time-consuming and demanding. In this study, a comparative analysis of the eight constraint handling mechanisms is carried out, guiding decision-makers in their optimization practices. The constraint-handling techniques are used along with the Whale Optimization Algorithm (WOA), and 19 real-life mechanical design problems, which are also part of the CEC2020 benchmark suite, are tested in the experimental analysis. The nonparametric statistical analysis incorporating Nemenyi and Holm post-hoc procedures shows that the inverse tangent constraint-handling and eclectic penalty methods exhibit high performance in real-life mechanical design problems. [ABSTRACT FROM AUTHOR]

• We analyze the effectiveness of the perturbations generated by popular adversarial attacks. • We present a formal comparison of the objective functions used by popular adversarial attacks. • We expose the limited optimization space of the cross-entropy loss and the cross-entropy sign loss. • We expose the detrimental effect of cross-entropy sign loss and logit loss on adversarial optimization. • We analyze how well neural networks can identify adversarial perturbations generated by adversarial attacks. After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical results on the effectiveness of adversarial example generation methods against defense mechanisms are discussed in detail in the literature, an in-depth study of the theoretical properties and the perturbation effectiveness of these adversarial attacks has largely been lacking. In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner's attack. Specifically, we perform a comparative and formal analysis of the loss functions underlying the aforementioned attacks while laying out large-scale experimental results on the ImageNet dataset. This analysis exposes (1) the faster optimization speed as well as the constrained optimization space of the cross-entropy loss, (2) the detrimental effects of using the signature of the cross-entropy loss on optimization precision as well as optimization space, and (3) the slow optimization speed of the logit loss in the context of adversariality. Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation. Moreover, our experiments show that the underlying loss function of Carlini & Wagner's attack, which is criticized for being substantially slower than other adversarial attacks, is not that much slower than other loss functions. Finally, we analyze how well neural networks can identify adversarial perturbations generated by the attacks under consideration, hereby revisiting the idea of adversarial retraining on ImageNet. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of nonphysician providers on measures of spatial access to primary care in Iowa, a state where physician assistants and advanced practice registered nurses are considered primary care providers.Data Sources: 2017 Iowa Health Professions Inventory (Carver College of Medicine), and minor civil division (MCD) level population data for Iowa from the American Community Survey.Study Design: We used a constrained optimization model to probabilistically allocate patient populations to nearby (within a 30-minute drive) primary care providers. We compared the results (across 10 000 scenarios) using only primary care physicians with those including nonphysician providers (NPPs). We analyze results by rurality and compare findings with current health professional shortage areas.Data Collection/extraction Methods: Physicians and NPPs practicing in primary care in 2017 were extracted from the Iowa Health Professions Inventory.Principal Findings: Considering only primary care physicians, the average unallocated population for primary care was 222 109 (7 percent of Iowa's population). Most of the unallocated population (86 percent) was in rural areas with low population density (< 50/square mile). The addition of NPPs to the primary care workforce reduced unallocated population by 65 percent to 78 252 (2.5 percent of Iowa's population). Despite the majority of NPPs being located in urban areas, most of the improvement in spatial accessibility (78 percent) is associated with sparsely populated rural areas.Conclusions: The inclusion of nonphysician providers greatly reduces but does not eliminate all areas of inadequate spatial access to primary care. [ABSTRACT FROM AUTHOR]

This paper proposes metaheuristic interval methods of global constrained optimization and their software implementation. Three methods are considered: average path endings, stochastic grid, and interval scatter search. The methods are applied to optimal program control design for nonlinear deterministic discrete and continuous systems. As an application the problem of three-dimensional interception is solved and a comparative analysis of efficiency is presented. [ABSTRACT FROM AUTHOR]

Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [ABSTRACT FROM AUTHOR]

Meta-models proved to be a very efficient strategy for optimization of expensive black-box models, e.g., finite element simulation for electromagnetic devices. It enables to reduce the computational burden for optimization purposes. Kriging is a popular method to build meta-model. Its statistical properties were first used in efficient global optimization for unconstrained problems. Afterward, many extensions were introduced in the literature to deal with constrained optimization. This paper presents a comparative study of some infill criteria for constraints handling and a new strategy for parallelization of the expensive computations of models. TEAM workshop problem 22 is taken as an electromagnetic test problem. [ABSTRACT FROM AUTHOR]

This work studies one multi-objective immune genetic algorithm with small population to solve a general kind of unconstrained multi-objective interval-valued programming. In this optimization approach, those competitive individuals are discriminated based on interval arithmetic rules and a possibility model; a crowding degree model in interval-valued environments is developed to eliminate redundant individuals; the current population promotes different individuals to evolve towards specific directions by population sorting and immune evolution, while those elitist individuals found accelerate to explore the desired regions through genetic evolution. The theoretical analysis has showed that the computational complexity of the proposed approach depends mainly on the elitist population size. Comparative experiments have illustrated that the approach can take a rational tradeoff between effect and efficiency. It can perform well over the compared approaches as a whole, and has the potential to solving multi-modal and hard multi-objective interval-valued programming problems. [ABSTRACT FROM AUTHOR]

The aim of this study was to gather, discuss, and compare recently developed metaheuristics to understand the pace of development in the field of metaheuristics and make some recommendations for the research community and practitioners. By thoroughly and comprehensively searching the literature and narrowing the search results, we created with a list of 57 novel metaheuristic algorithms. Based on the availability of the source code, we reviewed and analysed the optimization capability of 26 of these algorithms through a series of experiments. We also evaluated the exploitation and exploration capabilities of these metaheuristics by using 50 unimodal functions and 50 multimodal functions, respectively. In addition, we assessed the capability of these algorithms to balance exploration and exploitation by using 29 shifted, rotated, composite, and hybrid CEC-BC-2017 benchmark functions. Moreover, we evaluated the applicability of these metaheuristics on four real-world constrained engineering optimization problems. To rank the algorithms, we performed a nonparametric statistical test, the Friedman mean rank test. Based on the statistical results for the unimodal and multimodal functions, we declared that the GBO, PO, and MRFO algorithms have better exploration and exploitation capabilities. Based on the results for the CEC-BC-2017 benchmark functions, we found the MPA, FBI, and HBO algorithms to be the most balanced. Finally, based on the results for the constrained engineering optimization problems, we declared that the HBO, GBO, and MA algorithms are the most suitable. Collectively, we confidently recommend the GBO, MPA, PO, and HBO algorithms for real-world optimization problems. [ABSTRACT FROM AUTHOR]

Multi-objective optimization is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Real-life engineering designs often contain more than one conflicting objective function, which requires a multi-objective approach. In a single-objective optimization problem, the optimal solution is clearly defined, while a set of trade-offs that gives rise to numerous solutions exists in multi-objective optimization problems. Each solution represents a particular performance trade-off between the objectives and can be considered optimal. In this paper, the performance of a recently developed teaching–learning-based optimization (TLBO) algorithm is evaluated against the other optimization algorithms over a set of multi-objective unconstrained and constrained test functions and the results are compared. The TLBO algorithm was observed to outperform the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. [ABSTRACT FROM AUTHOR]

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems. [ABSTRACT FROM AUTHOR]

Abstract: A novel population-based algorithm based on the mine bomb explosion concept, called the mine blast algorithm (MBA), is applied to the constrained optimization and engineering design problems. A comprehensive comparative study has been carried out to show the performance of the MBA over other recognized optimizers in terms of computational effort (measured as the number of function evaluations) and function value (accuracy). Sixteen constrained benchmark and engineering design problems have been solved and the obtained results were compared with other well-known optimizers. The obtained results demonstrate that, the proposed MBA requires less number of function evaluations and in most cases gives better results compared to other considered algorithms. [Copyright &y& Elsevier]

Abstract: A simple three-term conjugate gradient algorithm which satisfies both the descent condition and the conjugacy condition is presented. This algorithm is a modification of the Hestenes and Stiefel algorithm (Hestenes and Stiefel, 1952) [10], or that of Hager and Zhang (Hager and Zhang, 2005) [23] in such a way that the search direction is descent and it satisfies the conjugacy condition. These properties are independent of the line search. Also, the algorithm could be considered as a modification of the memoryless BFGS quasi-Newton method. The new approximation of the minimum is obtained by the general Wolfe line search, now using a standard acceleration technique developed by Andrei (2009) [27]. For uniformly convex functions, under standard assumptions, the global convergence of the algorithm is proved. Numerical comparisons of the suggested three-term conjugate gradient algorithm versus six other three-term conjugate gradient algorithms, using a set of 750 unconstrained optimization problems, show that all these computational schemes have similar performances, the suggested one being slightly faster and more robust. The proposed three-term conjugate gradient algorithm substantially outperforms the well-known Hestenes and Stiefel conjugate gradient algorithm, as well as the more elaborate CG_DESCENT algorithm. Using five applications from the MINPACK-2 test problem collection (Averick et al., 1992) [25], with variables, we show that the suggested three-term conjugate gradient algorithm is the top performer versus CG_DESCENT. [Copyright &y& Elsevier]

Abstract: This paper presents results of a comparative study with the objective to identify the most effective and efficient way of applying a local search method embedded in a hybrid algorithm. The hybrid metaheuristic employed in this study is called “DE–HS–HJ” because it is comprised of two cooperative metaheusitic algorithms, i.e., differential evolution (DE) and harmony search (HS), and one local search (LS) method, i.e., Hooke and Jeeves (HJ) direct search. Eighteen different ways of using HJ local search were implemented and all of them were evaluated with 19 problems, in terms of six performance indices, covering both accuracy and efficiency. Statistic analyses were conducted accordingly to determine the significance in performance differences. The test results show that overall the best three LS application strategies are applying local search to every generated solution with a specified probability and also to each newly updated solution (NUS+ESP), applying local search to every generated solution with a specified probability (ESP), and applying local search to every generated solution with probability and also to the updated current global best solution (EUGbest+ESP). ESP is found to be the best local search application strategy in terms of success rate. Integrating it with NUS further improve the overall performance. EUGbest+ESP is the most efficient and it is also able to achieve high level of accuracy (the fourth place in terms of success rate with an average above 0.9). [Copyright &y& Elsevier]

Many real-world optimization problems comprise objective functions that are based on the output of one or more simulation models. As these underlying processes can be time and computation intensive, the objective function is deemed expensive to evaluate. While methods to alleviate this cost in the optimization procedure have been explored previously, less attention has been given to the treatment of expensive constraints. This article presents a methodology for treating expensive simulation-based nonlinear constraints alongside an expensive simulation-based objective function using adaptive radial basis function techniques. Specifically, a multiquadric radial basis function approximation scheme is developed, together with a robust training method, to model not only the costly objective function, but also each expensive simulation-based constraint defined in the problem. The article presents the methodology developed for expensive nonlinear constrained optimization problems comprising both continuous and integer variables. Results from various test cases, both analytical and simulation-based, are presented. [ABSTRACT FROM AUTHOR]

Abstract: Minimum weight dominating set (MWDS) finds many uses in solving problems as varied as clustering in wireless networks, multi-document summarization in information retrieval and so on. It is proven to be -hard, even for unit disk graphs. Many centralized and distributed, greedy and approximation algorithms have been proposed for the MWDS problem. However, all the approximation algorithms are limited to unit disk graphs which are primarily used to model wireless networks. This assumption fails when applied to other domains. In this paper, we present two metaheuristic algorithms – a hybrid genetic algorithm and a hybrid ant colony optimization algorithm – for the problem of computing minimum weight dominating set. We compare our results with that of a greedy heuristic as well as the only other metaheuristic algorithm proposed so far in the literature and show that our algorithms are far better than these algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a new optimization technique called water cycle algorithm (WCA) which is applied to a number of constrained optimization and engineering design problems. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. A comparative study has been carried out to show the effectiveness of the WCA over other well-known optimizers in terms of computational effort (measures as number of function evaluations) and function value (accuracy) in this paper. [Copyright &y& Elsevier]

Abstract: Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms. [Copyright &y& Elsevier]

. The focus of this article is on simultaneous confidence bands over a rectangular covariate region for a linear regression model with k>1 covariates, for which only conservative or approximate confidence bands are available in the statistical literature stretching back to Working & Hotelling ( J. Amer. Statist. Assoc. 24, 1929; 73-85). Formulas of simultaneous confidence levels of the hyperbolic and constant width bands are provided. These involve only a k-dimensional integral; it is unlikely that the simultaneous confidence levels can be expressed as an integral of less than k-dimension. These formulas allow the construction for the first time of exact hyperbolic and constant width confidence bands for at least a small k(>1) by using numerical quadrature. Comparison between the hyperbolic and constant width bands is then addressed under both the average width and minimum volume confidence set criteria. It is observed that the constant width band can be drastically less efficient than the hyperbolic band when k>1. Finally it is pointed out how the methods given in this article can be applied to more general regression models such as fixed-effect or random-effect generalized linear regression models. [ABSTRACT FROM AUTHOR]

Abstract: The irregular shape packing problem is approached. The container has a fixed width and an open dimension to be minimized. The proposed algorithm constructively creates the solution using an ordered list of items and a placement heuristic. Simulated annealing is the adopted metaheuristic to solve the optimization problem. A two-level algorithm is used to minimize the open dimension of the container. To ensure feasible layouts, the concept of collision free region is used. A collision free region represents all possible translations for an item to be placed and may be degenerated. For a moving item, the proposed placement heuristic detects the presence of exact fits (when the item is fully constrained by its surroundings) and exact slides (when the item position is constrained in all but one direction). The relevance of these positions is analyzed and a new placement heuristic is proposed. Computational comparisons on benchmark problems show that the proposed algorithm generated highly competitive solutions. Moreover, our algorithm updated some best known results. [Copyright &y& Elsevier]

Abstract: A modified Artificial Bee Colony algorithm to solve constrained numerical optimization problems is presented in this paper. Four modifications related with the selection mechanism, the scout bee operator, and the equality and boundary constraints are made to the algorithm with the aim to modify its behavior in a constrained search space. Six performance measures found in the specialized literature are employed to analyze different capabilities in the proposed algorithm such as the ability and cost to generate feasible solutions, the capacity and cost to locate the feasible global optimum solution and the competency to improve feasible solutions. Three experiments, including a comparison with state-of-the-art algorithms, are considered in the test design where twenty four well-known benchmark problems with different features are utilized. The overall results show that the proposed algorithm differs in its behavior with respect to the original Artificial Bee Colony algorithm but its performance is improved, mostly in problems with small feasible regions due to the presence of equality constraints. [Copyright &y& Elsevier]

For solving large-scale unconstrained minimization problems, the nonlinear conjugate gradient method is welcome due to its simplicity, low storage, efficiency and nice convergence properties. Among all the methods in the framework, the conjugate gradient descent algorithm — CG_DESCENT is very popular, in which the generated directions descend automatically, and this nice property is independent of any line search used. In this paper, we generalize CG_DESCENT with two Barzilai–Borwein steplength reused cyclically. We show that the resulting algorithm owns attractive sufficient descent property and converges globally under some mild conditions. We test the proposed algorithm by using a large set of unconstrained problems with high dimensions in CUTEr library. The numerical comparisons with the state-of-the-art algorithm CG_DESCENT illustrate that the proposed method is effective, competitive, and promising. [ABSTRACT FROM AUTHOR]

Abstract: Leveling is a process used to minimize the plate defects including flatness imperfections and uniformity of internal stress in the plate industry. It plays an important role in delivering the desired material properties and product standards required. This paper presents a new mechanics model for predicting the leveling process of plates. First, the mechanics model is developed based on the curvature integration method. Next, a global nonlinear unconstrained optimization method is utilized to solve this model. Then, the presented model is evaluated by comparative study to experimental data. Finally, the leveling process is studied using our model. It is found that the bending curvature of the plate is highly sensitive to the contact angles between the plate and the roller. The defective plate with different initial curvature tends to be flatted and the residual stress can be reduced effectively after being leveled. [Copyright &y& Elsevier]

In this paper, a set of new real-coded genetic algorithms (RCGAs) with local and global exploratory search capabilities are proposed. The search capabilities are based on the inclusion of a modified crossover (MC) procedure and a new global exploratory method in RCGA. The global exploratory method is based on vector projection while the MC procedure is based on a limited version of the pattern search method. These modifications are introduced to increase the efficiency and robustness of RCGAs through better local and global exploration of the search region. An experimental study of the new algorithms was carried out using a set of 57 test problems. Statistical analyses and comparisons of the new algorithms with standard real-coded genetic algorithm (SRCGA) and some recent global optimization algorithms were carried out. Results obtained show that the modifications remarkably improve the performance of RCGAs across the test problems. [ABSTRACT FROM AUTHOR]

In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches. [ABSTRACT FROM AUTHOR]

Abstract: The filled function method is an efficient approach for finding global minimizers of multi-dimensional and nonlinear functions in the absence of any restrictions. In this paper, we give a new definition of filled function and the idea of constructing a new filled function, and then a new class of filled functions with one parameter on the basis of the new definition, which possesses better quality, is presented. Theoretical properties of the new class of filled functions are investigated. A new algorithm is developed from the new filled function method. The implementation of the algorithm on seven test problems with dimensions up to 30 is reported, and comparisons with other filled function methods demonstrate that the new algorithm is more efficient. [Copyright &y& Elsevier]

Abstract: We propose a new constraint optimization energy and an iteration scheme for image segmentation which is connected to edge-weighted centroidal Voronoi tessellation (EWCVT). We show that the characteristic functions of the edge-weighted Voronoi regions are the minimizers (may not unique) of the proposed energy at each iteration. We propose a narrow banding algorithm to accelerate the implementation, which makes the proposed method very fast. We generalize the CVT segmentation to hand intensity inhomogeneous and texture segmentation by incorporating the global and local image information into the energy functional. Compared with other approaches such as level set method, the experimental results in this paper have shown that our approach greatly improves the calculation efficiency without losing segmentation accuracy. [Copyright &y& Elsevier]

Abstract: The p-center problem is one of the location problems that have been studied in operations research and computational geometry. This paper describes a compatible discrete space version of the heuristic Voronoi diagram algorithm. Since the algorithm gets stuck in local optimums in some cases, we apply a number of changes in the body of the algorithm with regard to the geometry of the problem, in a way that it can reach the global optimum with a high probability. Finally, a comparison between the results of these two algorithms on several test problems and a real-world problem are presented. [Copyright &y& Elsevier]

A decision feedback equalizer (DFE) is a nonlinear equalizer which utilizes the previous detector assessment to mitigate the intersymbol interference (ISI) on received symbols. Alternatively, we can say that the distortion on the current symbol that was caused by previous symbols is eliminated. While linear equalizer (LE) can be employed on the channels where the ISI is not significantly present but in case when there is a severe ISI channel then DFE's have been effectively used to mitigate ISI. Different techniques have been proposed to improve the decision feedback equalization performance. In this paper we have reviewed some of the existing equalization techniques and have highlighted their performance gains. [ABSTRACT FROM AUTHOR]

An analytical optimization approach of the submersible solenoid valve constrained in a specific small volume is presented. In order to maximum the electromagnetic force, thermal model and electromagnetic model are constructed, and five radial dimensions are optimized when the main limiting quantity is the maximum allowed temperature. A numerical optimization calculation is performed in order to verify the proposed analytical approach. The optimal solution of the numerical calculation is compared with the solution of the analytical approach, which shows a good agreement between the two methods. A thermal analysis of the optimal submersible solenoid valve based on AYSYS is also performed to verify the thermal model. A comparative study between the experimental and simulated static characteristics of the optimal submersible solenoid valve is also performed. The results show that the proposed analytical approach is correct. [ABSTRACT FROM AUTHOR]

In this paper, the design of the fixed-structure robust controllers satisfying multiple H\infty performance criteria is formulated as a non-convex multidimensional constrained optimization problem. An effective differential evolution with level comparison (DELC) is proposed, which applies differential evolution for global searching and employs level comparison for constraint handling. The DELC is general and can be easily and directly applied to many kinds of reduced-order/fixed-structure controller synthesis problems without any conservative assumption about the objective function. Numerical simulations are carried out based on some typical problems, and the comparisons with existing methods demonstrate the effectiveness, efficiency, and robustness of the proposed DELC algorithm. [ABSTRACT FROM PUBLISHER]

Abstract: Recently there have two different effective methods proposed by Kanzow et al. in (Kanzow, 2001 ) and (Kanzow and Petra, 2004 ), respectively, which commonly use the Fischer–Burmeister (FB) function to recast the mixed complementarity problem (MCP) as a constrained minimization problem and a nonlinear system of equations, respectively. They all remark that their algorithms may be improved if the FB function is replaced by other NCP functions. Accordingly, in this paper, we employ the generalized Fischer–Burmeister (GFB) where the 2-norm in the FB function is relaxed to a general -norm () for the two methods and investigate how much the improvement is by changing the parameter as well as which method is influenced more when we do so, by the performance profiles of iterations and function evaluations for the two methods with different on MCPLIB collection. [Copyright &y& Elsevier]

Abstract: In this paper, we propose some improvements on a new gradient-type method for solving large-scale unconstrained optimization problems, in which we use data from two previous steps to revise the current approximate Hessian. The new method which we considered, resembles to that of Barzilai and Borwein (BB) method. The innovation features of this approach consist in using approximation of the Hessian in diagonal matrix form based on the modified weak secant equation rather than the multiple of the identity matrix in the BB method. Using this approach, we can obtain a higher order accuracy of Hessian approximation when compares to other existing BB-type method. By incorporating a simple monotone strategy, the global convergence of the new method is achieved. Practical insights into the effectiveness of the proposed method are given by numerical comparison with the BB method and its variant. [Copyright &y& Elsevier]

Abstract: A quantitative biochronological study by Cody et al. (2008) integrates comprehensive diatom biostratigraphy, magnetostratigraphy, and tephrostratigraphy from 32 Neogene sections around the Southern Ocean and Antarctic continental margin. A recent method, known as Constrained Optimization (CONOP), which can be viewed as a multidimensional version of graphic correlation, is applied to that very interesting database. The goal of the present paper is to discuss some theoretical aspects of quantitative biochronology and to compare the constrained optimization with the deterministic method called Unitary Associations (UAM), a graph theoretical model. We illustrate the fact that the UAM is an extremely powerful and unique theory allowing an in-depth analysis of the internal conflicting inter-taxon stratigraphic relationships, inherent to any complex biostratigraphical database. [Copyright &y& Elsevier]

There are some practical optimization problems that can be only optimized using historical data, which is known as offline data-driven optimization problems. Since the real function evaluations are not available in the optimization process, surrogate models must replace the real fitness evaluations to guide the search. The key issue in offline data-driven optimization is how to make full use of offline data for building robust and accurate surrogate models. However, the existing offline algorithms do not consider optimization problems with constraints. Since the predicted values of the surrogate models for constraints are inaccurate, it becomes difficult to balance the objective function and the constraints during the optimization process. In this paper, we discuss a number of commonly used constraint handling techniques and combine them with an offline data-driven evolutionary algorithm. Also, four different test functions are designed with various constraints and difficulties. The results on the test function show that the multi-objective-based constraint handling technique is more likely to obtain feasible solutions, while stochastic ranking has better quality of feasible solutions. • The main challenges and research gap of offline data-driven constrained optimization are discussed. • Existing constraint handling techniques are embedded in offline data-driven evolutionary algorithm. • The performance of those constraint handling techniques for offline data-driven evolutionary optimization is studied via experiments. [ABSTRACT FROM AUTHOR]

This paper has two intertwined goals: (i) to study the feasibility of an atlas of gene expression data sets as a visual interface to expression databanks, and (ii) to study which dimensionality reduction methods would be suitable for visualizing very high-dimensional data sets. Several new methods have been recently proposed for the estimation of data manifolds or embeddings, but they have so far not been compared in the task of visualization. In visualizations the dimensionality is constrained, in addition to the data itself, by the presentation medium. It turns out that an older method, curvilinear component analysis, outperforms the new ones in terms of trustworthiness of the projections. In a sample databank on gene expression, the main sources of variation were the differences between data sets, different labs, and different measurement methods. This hints at a need for better methods for making the data sets commensurable, in accordance with earlier studies. The good news is that the visualized overview, expression atlas, reveals many of these subsets. Hence, we conclude that dimensionality reduction even from 1339 to 2 can produce a useful interface to gene expression databanks.Information Visualization (2007) 6, 139–154. doi:10.1057/palgrave.ivs.9500153 [ABSTRACT FROM AUTHOR]

This paper is focused on constrained nonlinear optimization problems that are encountered in engineering design. These problems often involve imprecise objectives as well as constraints and as such do not lend themselves well to the application of conventional nonlinear optimization techniques. In this paper, fuzzy logic, in conjunction with genetic algorithms, is used to address this issue. In particular, fuzzy sets are used to define the constraints=objective functions while a genetic search algorithm is used to find an approximation of the so-called pareto-optimal solution. It is shown, via the application of this approach to two classic problems in the field, that the proposed methodology is effective in terms of finding a solution that is within acceptable proximity of the pareto-optimal front. Moreover, a comparative analysis of the performance of the proposed approach with those reported in the literature reveals the computational efficiency of this methodology. [ABSTRACT FROM AUTHOR]

Background: The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice.Methods: We proposed a methodology for decision making that uses a multiple criteria decision analysis (MCDA) to support the clinician in the selection of an efficient combined empiric therapy. The MCDA includes a multi-objective constrained optimization model whose criteria are the maximum efficacy of therapy, maximum activity, the minimum activity overlapping, the minimum use of restricted antibiotics, the minimum toxicity of antibiotics and the activity against the most prevalent and virulent bacteria. The decision process can be defined in 4 steps: (1) selection of clinical situation of interest, (2) definition of local optimization criteria, (3) definition of constraints for reducing combinations, (4) manual sorting of solutions according to patient's clinical conditions, and (5) selection of a combination.Experiments and Results: In order to show the application of the methodology to a clinical case, we carried out experiments with antibiotic susceptibility tests in blood samples taken during a five years period at a university hospital. The validation of the results consists of a manual review of the combinations and experiments carried out by an expert physician that has explained the most relevant solutions proposed according to current clinical knowledge and their use.Conclusion: We show that with the decision process proposed, the physician is able to select the best combined therapy according to different criteria such as maximum efficacy, activity and minimum toxicity. A method for the recommendation of combined antibiotic therapy developed on the basis of a multi-objective optimization model may assist the physicians in the search for alternatives to the use of broad-spectrum antibiotics or restricted antibiotics for empirical therapy. The decision proposed can be easily reproduced for any local epidemiology and any different clinical settings. [ABSTRACT FROM AUTHOR]

Abstract: Presented here is a genetic algorithm that computes an approximate solution to constrained and unconstrained global optimization problems. This technique has been implemented using several pseudo- and quasi-random number generators and the results of several test examples are presented. The performance of this technique is based on a ranked comparison of relative error. [Copyright &y& Elsevier]