A new multi-section based technique for constrained optimization problems with interval-valued objective function

For the two last decades, people have tried to provide practical electronic cash schemes, with more or less success. Indeed, the most secure ones generally suffer from inefficiency, largely due to the use of restrictive blind signatures, on the other hand efficient schemes often suffer from serious security drawbacks. In this paper, we propose both a new tool providing scalable anonymity at a low cost, and a new Internet business: Anonymity Providers.Those Anonymity Providers certify re-encrypted data after having been convinced of the validity of the content, but without knowing anything about this latter. It is a very useful third party in many applications (e.g. for revocable anonymous electronic cash, where a coin would be a certified encryption of the user's identity, such that a Revocation Center, and only it, can recover this identity, if needed).With this new tool, each user can get the required anonymity level. depending on the available time, computation and/or money amounts. Furthermore, the Anonymity Provider may be a new type of business over the Internet, profitable for everybody: - from the provider point of view as he can charge the service; - from the user point of view as he can obtain a high level of anonymity at low computational cost. Moreover, a user who does not require anonymity has no extra computation to perform.This technique is quite efficient because of its optimistic orientation: in case of honest use, everything is very efficient. Some slightly more heavy processes have to be performed in case of fraud detection, but with overwhelming tracing success.

This paper develops a new surrogate-assisted evolutionary programming (EP) algorithm for computationally expensive constrained black-box optimization. The proposed algorithm, TRICEPS (Trust Regions In Constrained Evolutionary Programming using Surrogates) builds surrogates for the black-box objective function and inequality constraint functions in every generation of the EP and uses a trust-region-like approach to refine the best solution at the end of each generation. Each parent produces a large number of trial offspring in each generation, and then the surrogates are used to identify promising trial offspring, which become the actual offspring where the objective and constraint functions are evaluated. After the function evaluations at these offspring, TRICEPS finds a minimizer of the surrogate of the objective function within a trust region centered at the current best solution and subject to surrogate inequality constraints with a small margin and with a distance requirement from previously evaluated points. The trust region is either expanded or reduced depending on whether the subproblem solution turned out to be feasible and whether the ratio of the actual improvement to the predicted improvement exceeds or falls below certain thresholds. TRICEPS is implemented using a cubic radial basis function (RBF) model with a linear polynomial tail and is compared to an RBF-assisted EP called CEP-RBF (Regis 2014b) and to other alternatives on 18 benchmark problems and on an automotive application with 124 decision variables and 68 black-box inequality constraints. Performance and data profiles show that TRICEPS is a substantial improvement over CEP-RBF and it is much better than the other alternatives on the test problems used.

In this paper, from the point of view reducing the system calculation cost, a digital channelized receiver is proposed. The decimation factor of the proposed digital channelized receiver with low calculation cost is equal to the number of signal channels, which is acquired by changing the input signal of the embedded instantaneous frequency measurement receiver. Due to the increase of the system decimation factor, the data to be processed are decreased and the system processing speed is improved indirectly. In this paper, the architecture and working principle of the proposed digital channelized receiver with low calculation cost is firstly given; secondly, the overlapping channel division method used in the digital channelized receiver with low calculation cost is put forward. Meanwhile, the channel arbitration method of the unmodulated or modulated signal in the case of overlapping channel division is analyzed. Then, the frequency measurement principle of the embedded instantaneous frequency measurement receiver is deduced in detail. Through the use of the overlapping channel division method, the proposed digital channelized receiver with low calculation cost is able to avoid the existence of blind spots between channels and increases the interception probabilities of the input signals. And, the proposed digital channelized receiver can distinguish two signals coming simultaneously to the same channel by using the overlapping channel division method. In addition, the channel of the input signal truly residing can be simply determined by using the channel arbitration in the case of overlapping channel division and according to the output frequency of the embedded instantaneous frequency measurement receiver. Finally, the related simulation results verify the correctness and feasibility of the theories and method involved in this paper.

To represent practical problems appropriately, many mathematical optimization models require equality constraints in addition to inequality constraints. The existence of equality constraints reduces the size of the feasible space, which makes it difficult to locate feasible and optimal solutions. This paper shows the enhanced performance of an agent-based evolutionary algorithm in solving Constrained Optimization Problems (COPs) with equality constraints. In the early generations of the evolutionary process, the agents use a new learning process that is specifically designed for handling equality constraints. In the later generations, the agents improve their performance through other learning processes by exploiting their own potential. The performance of the proposed algorithm is tested on a set of well-known benchmark problems including two new problems. The experimental results confirm the improved performance of the proposed algorithm.

Intelligent sensitivity analysis framework based on machine learning for spacecraft thermal design

With low computation cost, motion vectors can be readily extracted from MPEG video streams and processed to estimate vehicle motion speed. A statistical model is proposed to model vehicle speed and noise. In order to achieve high estimation accuracy and also study the limitations of the proposed algorithm, we quantitatively evaluated four parameters used in our algorithm: temporal filter window size T, video resolution R v (CIF/QCIF), motion vector frame distance m, and video bit-rates. Our experiments showed that the mean vehicle speed can be estimated with high accuracy, up to 85 to 92% by proper spatial and temporal processing. The proposed algorithm is especially suitable for Skycam-based application, where the traditional tracking-based or virtual-loop-based approaches perform poorly because of their requirements of high-resolution images. Although extensive work has been done in extracting motion information directly from MPEG video data in compressed domain, to our best knowledge, this paper is the very first work in which stationary motion (speed) of moving objects can be estimated with high accuracy directly from MPEG motion vectors. Furthermore the proposed method is not limited to vehicle speed estimation by nature and it can be applied to other applications where the stationary motion assumption is satisfied.

In this paper we propose a new technique for calibrating a camera with very high accuracy and low computational cost. The geometric camera parameters considered include camera position, orientation, focal length, radial lens distortion, pixel size, and optical axis piercing point. With our method, the camera parameters to be estimated are divided into two parts: the radial lens distortion coefficient (kappa) and a composite parameter vector c composed of all the above geometric camera parameters other than (kappa) . Instead of using nonlinear optimization techniques, the estimation of (kappa) is transformed into an eigenvalue problem of a 8 X 8 matrix. Our method is fast since it requires only linear computation. It is accurate since the effect of the lens distortion is considered and all the information contained in the calibration points is used. Computer simulation and real experiment have shown that the performance of our calibration method is better than that of the well-known method proposed by Tsai.

Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach

In the application of optimization techniques, considerable difficulty is often encountered in determining the most applicable or effective method for solving a particular problem. In an attempt to solve this dilemma, M. J. Box developed the Complex Method, which can be used in solving a broad spectrum of problems. The method is a multivariable, direct-search technique that is efficient and convenient in optimizing problems with nonlinear objective functions subject to inequality constraints on explicit or implicit variables. This method has been applied successfully to many problems in the chemical industry; however, the applicability of this method for solving oilfield problems apparently has not been recognized. In this paper, two oilfield problems, a push-pull steam stimulation process problems, a push-pull steam stimulation process and a well-drilling schedule, were optimized using the Complex Method. The results were compared with those obtained by other optimization techniques reported in the literature. For both problems, the Complex Method was as efficient as other methods and probably was more efficient in obtaining the global optimum, both in terms of the number of objective-function evaluations required and of computing time. The solution to the constrained push-pull steam stimulation process was obtained push-pull steam stimulation process was obtained in one-fourth the number of evaluations required with the Hooke and Jeeves search technique; the solution to the drilling problem with 48 variables was obtained in only 2,123 objective-function evaluations, or 29 seconds of computing time on an IBM 370/165. Through the solution of two problems, the Complex Method was demonstrated to be easily applied and effective in obtaining optimum solutions as compared with other methods. This method should be implemented easily in obtaining optimum solutions as compared with other methods. This method should be implemented easily in obtaining optimum solutions to other oilfield problems. Introduction In recent years, the application of optimization techniques to oilfield problems has become widespread because of the possibility of increased profits or reduced costs with implementation of such studies. Several types of oilfield problems have been optimized using a wide variety of optimization techniques. E. L. Dougherty conducted an extensive review of the major applications of optimization techniques to oilfield problems. in these studies, the authors used a variety problems. in these studies, the authors used a variety of optimization techniques because of the difficulty of selecting an effective method for a particular problem. The Complex Method is a general optimization technique that can be applied directly to a variety of nonlinear problems with inequality constraints. The method is a problems with inequality constraints. The method is a multivariable direct-search technique; thus, the application of this method is especially convenient when derivatives of the objective function are difficult to obtain. The broad applicability of the Complex Method has been demonstrated in the chemical industry by successfully optimizing problems ranging from absorber-strippers to nonisothermal polymerization reactors. The method is implemented easily and is efficient in obtaining the global optimum while imposing inequality constraints on explicit and implicit variables. The objective of this paper is to demonstrate the usefulness and effectiveness of the Complex Method in obtaining the optimum values for oilfield problems, as compared with other methods. Two oilfield problems that previously have been solved by other methods were optimized using the Complex Method. The sample problems were the following:the optimum cycle length and the amount of steam injected in successive cycles are determined for a push-pull steam stimulation process such that the cumulative discounted net income is maximized; andthe optimum set of rotary speeds and weights on a bit is determined such that the drilling cost per foot is minimized in a drilling operation. These problems were selected to illustrate the wide range of mathematical formulations for which the Complex Method is applicable. The variable weight-speed schedule was selected for optimization rather than the constant weight-speed schedule because of the complexity of the variable weight-speed problem. SPEJ p. 123

The flexible open shop scheduling problem is the combination of two classical open shop and a parallel machine scheduling problems. Due to the computational complexity of solving the open shop scheduling problem, so far, limited research has considered the assumption of flexibility in this problem. In this study, a bi-objective flexible open shop scheduling with independent setup time is considered. The considered objective functions are to minimize the maximum completion time of jobs and total tardiness. Using the first objective function, an attempt is made to balance the amount of workloads on the machines so that the completion time of the last job is minimized, while using the second one, from the customers' point of view, an attempt is made to reduce the amount of tardiness in delivery of jobs to the customers. Here, firstly, a mixed-integer nonlinear programming model is proposed. Then, to deal with the multi-objective problem, the weighted Lp-metric method is used. Since, this problem is a non-deterministic polynomial-time hard problem (NP-hard), a scatter search algorithm is developed to obtain near-optimal solutions in a reasonable time. In this regard, for the first time, a novel solution representation method is also presented. To ensure the efficiency of the proposed algorithm, the results were compared with NSGA-II using several instances. After analyzing the results using four evaluation metrics, it is observed that the scatter search algorithm performs better than the NSGA-II.

Microgrid dynamic combined power–heat economic-emission dispatch with deferrable loads and price-based energy storage elements and power exchange

American Journal of Agricultural EconomicsVolume 68, Issue 5 p. 1219-1221 Invited Papers Session Optimal Control of General Equilibrium Models: Discussion Richard E. Howitt, Richard E. Howitt professor Department of Agricultural Economics, University of California, DavisSearch for more papers by this author Richard E. Howitt, Richard E. Howitt professor Department of Agricultural Economics, University of California, DavisSearch for more papers by this author First published: 01 December 1986 https://doi.org/10.2307/1241879AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume68, Issue5December 1986Pages 1219-1221 RelatedInformation

We consider a general multi-agent convex optimization problem where the agents are to collectively minimize a global objective function subject to a global inequality constraint, a global equality constraint, and a global constraint set. The objective function is defined by a sum of local objective functions, while the global constraint set is produced by the intersection of local constraint sets. In particular, we study two cases: one where the equality constraint is absent, and the other where the local constraint sets are identical. We devise two distributed primal-dual subgradient algorithms based on the characterization of the primal-dual optimal solutions as the saddle points of the Lagrangian and penalty functions. These algorithms can be implemented over networks with dynamically changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically agree on optimal solutions and optimal values of the optimization problem under the Slater's condition.

This paper aims to obtain new Karush–Kuhn–Tucker optimality conditions for solutions to semi-infinite interval equilibrium problems with interval-valued objective functions. The Karush–Kuhn–Tucker conditions for the semi-infinite interval programming problem are particular cases of those found in this paper for constrained equilibrium problem. We illustrate this with some examples. In addition, we obtain solutions to the interval equilibrium problem in the unconstrained case. The results presented in this paper extend the corresponding results in the literature.

This paper investigates how optimal controle can support weed management by looking specifically at an optimal control problem with an interval-valued objective function based on dynamic optimization. The dynamic optimization problem involves continuous variables modeled as an interval-valued nonlinear programming (INLP) problem. In this study, we converted the INLP problem into into a multi-objective problem using the concept of order relation and solver the multi-objetive problem using the weight method. We solved the resultant NLP problem with an active-set algorithm (ASA) for box-constrained optimization. Finally, we carried out an application in a weed management problem.