Fuzzy Evolutionary Algorithm Research Articles

Uncertainty-tolerant scheduling strategies for grid computing: knowledge-based techniques with bio-inspired learning

Abstract Nowadays, diverse areas in science as high energy physics, astronomy or climate research are increasingly relying on experimental studies addressed with hard computing simulations that cannot be faced with traditional distributed systems. In this context, grid computing has emerged as the new generation computing platform based on the large-scale cooperation of resources. Furthermore, the use of grid computing has also been extended to several technology, engineering or economy areas such as financial services and construction engineering that demand high computer capabilities. Nevertheless, a major issue in the sharing of resources is the scheduling problem in a high-dynamic and uncertain environment where resources may become available, inactive or reserved over time according to local policies or systems failures. In this paper, a review of scheduling strategies dealing with uncertainty in systems information by the application of techniques such as fuzzy logic, neural networks or evolutionary algorithms is presented. Furthermore, this work is centered on the study of scheduling strategies based on fuzzy rulebased systems given their flexibility and ability to adapt to changes in grid systems. These knowledge-based strategies are founded on a fuzzy characterization of the system state and the application of the scheduler knowledge in the form of fuzzy rules to cope with the imprecise environment. Obtaining good rules also arises as a challenging problem. Hence, the main learning methods that allow the improvement and adaptation of the expert schedulers are introduced.

Design of a robust layout with information uncertainty increasing over time: A fuzzy evolutionaryapproach

One of the problems encountered in the design of manufacturing systems is how to arrange the machines on the surface of the workshop, which is commonly referred to as a layout problem. Such a problem has been widely investigated in the literature. Most approaches use optimization technique to determine the position of each facility, assuming that the required data is available. Unfortunately, this assumption is often unrealistic, since the study design of a workshop is obviously conducted much before it is operating, so that data related to customer demands, for example, is generally not known with enough precision. Indeed, if good forecasts about what is to be produced in the next weeks can be available, they will obviously become more and more unreliable as the considered period of time will increase, so that layout found using classical approaches can turn out not to be relevant on the medium or long term. We propose an approach to design a robust layout in a context where the certainty of the information available decreases over time, which is usually the case for real applications. We propose a resolution approach based on a fuzzy evolutionary algorithm, which includes uncertain customer demands for each product. We show how this problem can be stated as a fuzzy dynamic layout problem with growing uncertainty over time. We suggest an evolutionary algorithm with adapted operators. Their performances are first tested using 2crisp layout problems already published. Then the impact of increasing uncertainty is studied using a suggested benchmark. The results of our experiments show the importance of considering the degradation of the information for designing robust layouts.

A Fuzzy Differential Evolution Scheduling Algorithm Based on Grid

Aim at the problem of tasks scheduling problem for grid computing based on fuzzy constraints, propose tasks scheduling model of grid computing about tasks scheduling problem. Using fuzzy AHP(analytic hierarchy process) processes fuzzy constraints weight value. Utilize the merit of differential evolutionary algorithm, that is, the algorithm don’t need objective function is continuous and differentiable. Propose fuzzy differential evolutionary algorithm, the algorithm can process tasks scheduling for grid computing based on fuzzy constraint. In the experiments, compare the proposed algorithm with the existing algorithms. the proposed algorithm is more favorable than the existing algorithm in term of reliability, security and drooped task numbers.

Selected Papers from SCIS & ISIS 2010 – No.2

This second special issue from the Joint 5th International Conference on Soft Computing and Intelligent Systems and the 11th International Symposium on Advanced Intelligent Systems (SCIS & ISIS) 2010 presents 11 progressive papers covering topics from Fuzzy c-Means, clustering, evolutionary algorithms, and self-organizing maps to optimization, spiral phenomena, matroids, multirobot systems, and reinforcement learning. This wide range of research interests reflects current trends in the SCIS & ISIS conference series. As such, they demonstrate how near-future research interests are gradually moving toward exciting new directions. We thank the authors and reviewers for taking time out from their busy schedules to make this publication possible. We are especially grateful to Prof. Toshio Fukuda of Nagoya University and to Prof. Kaoru Hirota of the Tokyo Institute of Technology for giving us the opportunity to guest-edit JACIII. Our special thanks go to Ms. Reiko Ohta of Fuji Technology Press Ltd. for her ongoing support in publication.

QoS scheduling of fuzzy strategy grid workflow based on the bio-network

Grid workflow may construct new value-added services by composing existing elementary services with sophisticated workflow logic. Grid workflow schedule requires sophisticated expertise of domain in order to automate the process of managing workflows and its components. Quality of service (QoS) becomes essential and poses great challenges to grid workflow. QoS-aware selection and execution of grid workflow can fulfil customer's expectations. A fuzzy strategy modelling approach is proposed to design the grid workflow schedule, which is inspired by the characteristics of emergence and self-evolution in biological immune system. The grid workflow matching relation is achieved by bio-entities (mobile agents) negotiation based on projection-join closure to capture such message situations. We propose a fuzzy preferences evolutionary algorithm to resolve the QoS-aware selection optimisation, which enables the construction of QoS-aware workflow at both abstract and concrete service levels. The simulation results show that the approach can significantly improve performance. It adapts well to the changes of environments.

Shot retrieval based on fuzzy evolutionary aiNet and hybrid features

As the multimedia data increasing exponentially, how to get the video data we need efficiently become so important and urgent. In this paper, a novel method for shot retrieval is proposed, which is based on fuzzy evolutionary aiNet and hybrid features. To begin with, the fuzzy evolutionary aiNet algorithm proposed in this paper is utilized to extract key-frames in a video sequence. Meanwhile, to represent a key-frame, hybrid features of color feature, texture feature and spatial structure feature are extracted. Then, the features of key-frames in the same shot are taken as an ensemble and mapped to high dimension space by non-linear mapping, and the result obeys Gaussian distribution. Finally, shot similarity is measured by the probabilistic distance between distributions of the key-frame feature ensembles for two shots, and similar shots are retrieved effectively by using this method. Experimental results show the validity of this proposed method.

A novel approach to developing and evaluating regulation strategies for urban transport disrupted networks

The current paper deals with the real-time regulation of an urban collective transport disrupted network, which is a very difficult problem, especially in the case of simultaneous disturbances (breakdowns, strikes, demonstrations, etc.). Indeed, regulators or decision makers have to carry out difficult tasks, which involves the assistance of a decision support system (DSS). The current paper presents the main part of this DSS, i.e. a regulation strategy generator and evaluator. Thus, a new hybrid approach is proposed using a fuzzy logic and evolutionary algorithm. It treats the regulation problem as an optimization one and provides the regulator with classified effective decisions by taking into account his/her preferences.

Soft Computing Techniques for Medical Image Analysis

The use of soft computing techniques for medical image analysis is reviewed in this paper. These techniques involve application of fuzzy logic, neural network and evolutionary algorithms. We introduce the different imaging modalities and motivation for using soft computing techniques for medical image analysis. We review different soft computing based approaches for image processing and segmentation, which can improve the diagnostic value of the images. We also present some results with MRI images. Based on these approaches we suggest an architectural framework for soft computing based application development.

Cellular neural networks and computational intelligence in medical image processing

The principal constituents of computational intelligence are fuzzy logic, neural networks and evolutionary algorithms, with emphasis in their mutual enhancement. The present paper reviews some applications of these formalisms in the area of medical image processing, where advantage is taken from the ability of fuzzy logic to work with imprecise information, the ability of neural networks to learn a system’s behavior from representative examples and the ability of evolutionary algorithms to optimize complex systems, particularly when no mathematical model is available. The paper focuses mainly on neural networks in medical image processing. A special kind of cellular neural networks based on multiple valued threshold logic in the complex plane will be presented and its efficacy for medical imaging will be documented. q 2001 Elsevier Science B.V. All rights reserved.

進化的アルゴリズムとファジィ理論の組合せの最近の動向(〈特集〉進化的アルゴリズムとファジィ理論)

進化的アルゴリズムとファジィ理論の組合せの最近の動向(〈特集〉進化的アルゴリズムとファジィ理論)