A methodological approach to optimizing RFID deployment

The interest of adopting RFID continues to grow in many industries, ranging from supply chain automation to healthcare management. However, dynamics of the operating environment is one of the major challenges that impede RFID deployment. Even though numerous researchers focus on controlled laboratory experiments to enhance the success of deployment, it is found that system performance in the actual production environment may differ significantly from that conducted in a controlled laboratory, resulting in poor deployment result. To cope with this situation, this paper proposes an RFID Deployment Optimizer (RFIDDO), which is a generic methodology for optimizing the RFID configuration to provide objective, quantifiable data about the data capture performance of RFID readers for comparing and optimizing RFID applications in a scientific manner. A case study has also been conducted in a logistics company to demonstrate the implementation of RFIDDO and provide contextual details to help other firms in coping with the environmental dynamics in the journey of RFID deployment.     

Structural analysis: A new approach to flow analysis in optimizing compilers

In this paper we present a new technique for analyzing the control flow of a computer program. This technique, called structural analysis, extends new interval analysis techniques and produces a program representation in which structured control-flow patterns are detected and recorded. This representation supports data-flow analysis elimination techniques similar to Rosen's high-level data-flow analysis technique, which are faster than interval-based methods. Morever, these results indicate that flow-graph based program analysis and direct analysis of the program's parse-tree can be performed by essentially the same methods, making uniform data-flow analysis procedure for optimizing compilers possible.     

An approach to optimizing software development team size

This paper explores the relationship between software size, development effort and team size. We propose an approach aimed at finding the team size where the project effort has its minimum. The approach was applied to the ISBSG repository containing nearly 4000 software projects. Based on the results we provide our recommendation for the optimal or near-optimal team size in seven project groups defined by four project properties.     

A statistical minimax approach to optimizing linear models under a priori uncertainty conditions

A statistical minimax method for optimizing linear models with parameters, given up to the accuracy of belonging to some uncertainty sets, is proposed. Statistical methods for constructing uncertainty sets as confidence regions with a given reliability level are presented. A numerical method for finding a minimax strategy is proposed for arbitrary uncertainty sets that meet convexity and compactness conditions. A number of examples are considered that admit the analytical solution to optimization problem. Results of numerical simulation are given.     

A GA-based approach for optimizing single-part flow-line configurations of RMS

Generating economical single-part flow-line (SPFL) configurations as candidates for a given demand period is an important optimization problem for reconfigurable manufacturing systems (RMS). The optimization problem addresses the questions of selecting number of workstations, number and type of paralleling identical machines as well as operation setups (OSs) for each workstation. The inputs include a precedence graph for a part, relationships between OSs and operations, machine options for each OS. The objective is to minimize the capital costs of the SPFL configurations. A 0–1 nonlinear programming (NLP) model is developed to handle the key issue of sharing machine utilization over consecutive OSs which is ignored in existing 0–1 integer linear programming (ILP) model. Then a GA-based approach is proposed to identify a set of economical solutions. To overcome the complexity of search space, a novel procedure is presented to guide GA to search within a refined solution space comprising the optimal configurations associated with feasible OS sequences. A case study shows that the best solution derived from the 0–1 NLP model through GA is better than the optimum of existing 0–1 ILP model. The results illustrate the effectiveness of our model and the efficiency of the GA-based approach.     

A new optimizing parameter approach of LSSVM multiclass classification model

The parameter values of kernel function affect classification results to a certain extent. In the paper, a multiclass classification model based on improved least squares support vector machine (LSSVM) is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function, and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in multiclass classification, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of classification. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best multiclass classification results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the model.     

A consultation system for insulin therapy

This paper describes a computer system to advice on insulin therapy for diabetic in-patients. A mathematical model was developed to describe the effect of insulin on blood glucose (BG) level. The system uses an adaptive approach to analyse the response to an applied insulin dosage. It learns the patient's individual parameters. All conventional injection and insulin pump regimens are supported. The individualised model is used to predict BG level of the proposed insulin dosage. The system uses a generate-reject strategy to output optimum insulin therapy in terms of optimum BG. The predictive capability of the system was tested and it is able to predict BG with a precision of 2.5 mmol/l after 3 days and 6 days of insulin pump treatment and conventional injection therapy, respectively.     

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings.     

A neuro-genetic approach for modeling and optimizing a complex cogeneration process

Cogeneration is the simultaneous generation of electricity and useful heat with the aim of exploiting more efficiently the energy stored in the fuel. Cogeneration is, however, a complex process that encompasses a great amount of sub-systems and variables. This fact makes it very difficult to obtain an analytical model of the whole plant, and therefore providing a mechanism or a methodology able to optimize the global behavior. This paper proposes a neuro-genetic strategy for modeling and optimizing a cogeneration process of a real industrial plant. Firstly, the modeling of the process is carried out by means of several interconnected neural networks where, each neural network deals with a particular sub-system of the plant. Next, the obtained models are used by a genetic algorithm, which solves a multiobjective optimization problem of the plant, where the goal is to minimize the fuel consumption and maximize both the generated electricity and the use of the heat. The proposed approach is evaluated with data of a real cogeneration plant collected over a one-year period. Obtained results show not only that the modeling of the plant is correct but also that the optimization increases significantly the efficiency of the cogeneration plant.     

A novel evolutionary approach for optimizing content-based image indexing algorithms.

Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.     

An approach to automation of optimizing transformations of algorithms and programs

The paper describes the construction principles of the DIAMANT block — an interactive transformation machine. The functional capabilities of the block are demonstrated in application to formal transformation (optimization) of the shuttle sort algorithm as generated in the MUL'TIPROTSESSIST system.Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 127–137, September–October, 1991.     

A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification.

In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nucleii in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.     

An approach for optimizing multiresponse simulation models

This paper describes a new procedure for optimizing multi-response simulation models. This method is based on the gradient projection technique and the use of a value function.     

A bargaining-driven global QoS adjustment approach for optimizing service composition execution path

In service-based workflow area, service composition is emerging as a promising technology providing more flexible integration means across a variety of distributed heterogeneous applications. A critical research issue in service composition is how to achieve an optimized overall end-to-end quality of service (QoS) composition’s requirements by effectively coordinating QoS constraints of individual service. However, few of existing representative works have well considered economic strategies closely related to competitive market scenarios to address overall QoS issues in service composition. In this paper, we propose a novel global QoS adjustment approach by employing a recursive bargaining strategy to gradually remove QoS constraint violations for optimizing service composition execution path. Our approach mainly exploits the hidden marketplace competitive techniques from economics for developing a novel bargaining strategy. Based on this strategy, a user agent can simultaneously negotiate with more than one provider for driving them into competition. As a result, concessions may be made by service providers depending on their own economic equilibrium theories to offer better QoS values than their initial proposals and thus a user can win more profits. Through recursively using bargaining strategy, critical nodes of an initially built optimal service composition execution path by local optimization policy can be continuously updated to minimize QoS constraint violations by reselecting better service providers. Eventually, a near-optimal service composition execution path can be reconstructed meeting user’s overall QoS requirements. Meanwhile, an experiment and evaluation have been conducted for the sake of demonstrating the feasibility and effectiveness of our proposed approach.     

An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system

With the rapid development in Information Technology (IT), the Internet has become one of the central media for conducting learning. However, most of the existing web-based learning systems only provide stand-alone subject materials for browsing and may face some drawbacks. For example, if students encounter problems during the learning process, their learning performances could be significantly devastated due to no instant aid. As an on-line learning system may interact with thousands of students, it is almost impossible for the teachers or the teaching assistants to answer all the students’ questions manually, which is not only inefficient, but also human laborious. To cope with this problem, an e-learning system that is able to automatically answer the students’ questions on the fly based on the training cases given by the teacher will be presented in this paper. Moreover, an enhanced genetic approach is proposed to optimize the weights of keywords for each candidate answer according to the feedbacks provided by the students, hence more accurate answers can be provided in the future. Experimental results have shown that the developed system can provide more accurate answerers than existing approaches by employing the self-adjusting method.     

New approach to stochastic optimizing control of steady-state systems using dynamic information

An underlying relationship between a dynamic stochastic control system and its steady-state counterpart is thoroughly investigated. A novel approach which uses both dynamic and steady-state information for solving the stochastic steady-state optimizing control problem is presented. A two-stage steady-state system identification technique is proposed and employed in the presented approach to estimate the expectation of the real process derivative. Compared with integrated system optimization and parameter estimation (ISOPE) methods, this new approach has reduced significantly the required controller set point changes and is much less sensitive when subjected to noise. Optimality of the algorithm is examined and sufficient conditions for global convergence are provided. Consistency and asymptotic behaviour of the two-stage identification method are also investigated. Computer simulations are provided and comparisons are made between the ISOPE method and the presented approach. Simulation results show that this new approach is very efficient and reliable.     

Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers

A new method for optimizing complex engineering designs is presented that is based on the Learnable Evolution Model (LEM), a recently developed form of non‐Darwinian evolutionary computation. Unlike conventional Darwinian‐type methods that execute an unguided evolutionary process, the proposed method, called LEMd, guides the evolutionary design process using a combination of two methods, one involving computational intelligence and the other involving encoded expert knowledge. Specifically, LEMd integrates two modes of operation, Learning Mode and Probing Mode. Learning Mode applies a machine learning program to create new designs through hypothesis generation and instantiation, whereas Probing Mode creates them by applying expert‐suggested design modification operators tailored to the specific design problem. The LEMd method has been used to implement two initial systems, ISHED1 and ISCOD1, specialized for the optimization of evaporators and condensers in cooling systems, respectively. The designs produced by these systems matched or exceeded in performance the best designs developed by human experts. These promising results and the generality of the presented method suggest that LEMd offers a powerful new tool for optimizing complex engineering systems. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1217–1248, 2006.     

A novel approach for optimizing the optical performance of the broadband tap coupler

An integrated approach using neural networks, exponential desirability functions and genetic algorithms to optimize parameter design problems with multiple responses is presented. The proposed approach aims to identify the input parameter settings to maximize the overall minimal satisfaction level with respect to all the responses. The proposed approach is illustrated by optimizing the fused process parameters created during fused biconic taper coupler development to improve the performance and reliability of a 1% (1199) single-window broadband tap coupler. The proposed solution procedure was implemented on a Taiwanese manufacturer of fibreoptic passive components. The implementation results demonstrate the practicability of the method. Comparison analysis revealed that the proposed procedure outperformed the traditional Taguchi method in resolving multi-response parameter design problems.