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A general framework based on dynamic multi-objective evolutionary algorithms for handling feature drifts on data streams

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 42-52页

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Dept Comp Engn TR-34722 Istanbul Turkey

This paper proposes a new and efficient framework to deal with the classification of data streams when exhibiting feature drifts. The first building block of the framework is a dynamic multi-objective evolutionary algorithm called dynamic Filter-Based Feature Selection (DFBFS) algorithm, which handles feature drifts by continuously selecting the optimal set during the stream processing. Moreover, a new feature drift detection method is proposed to incorporate with the DFBFS algorithm. In the proposed framework, the Artificial Neural Network (ANN) is utilized to classify the data streams by only focusing on the features selected by the DFBFS algorithm. The empirical study for evaluating the framework performance utilizes four different dataset generators by varying environmental parameters in terms of change severity and change frequency. Experimental evaluation validates our framework, as it significantly outperforms reference algorithms in terms of classification accuracy and the ability of fast recovery after the occurrence of feature drifts on the evaluated datasets. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Feature drift Classification of data streams dynamic multi-objective evolutionary algorithms Filter-based feature selection

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Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

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APPLIED SOFT COMPUTING 2019年 85卷 105783-000页

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Fac Engn Comp Engn Dept TR-34722 Istanbul Turkey

Characterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi-objective optimization problems (DMOPs), they do not sufficiently attract attentions of the research community. In this paper, we propose a set of new sensor-based change detection schemes for the DMOPs that significantly outperform the current used change detection schemes. Additionally, a new technique is proposed for detecting the change severity for DMOPs. The experimental evaluation based on different test problems and change severity levels validates performance of our technique. We also propose a novel adaptive algorithm called change-responsive NSGA-II (CR-NSGA-II) algorithm that incorporates the change detection schemes, the technique for change severity and a new response mechanism into the NSGA-II algorithm. Our algorithm demonstrates competitive and significantly better results than the leading DMOEAs on majority of test problems and metrics considered. (C) 2019 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization problems dynamic multi-objective evolutionary algorithms Change detection Characterization of change

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dynamic multi-objective Workflow Scheduling for Cloud Computing Based on evolutionary algorithms 11

Dynamic Multi-Objective Workflow Scheduling for Cloud Comput...

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11th IEEE/ACM International Conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Ismayilov, Goshgar Topcuoglu, Haluk Rahmi Marmara Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781728103594

Cloud computing is a dominant heterogeneous and distributed system, that offers on-demand resource capacity for different requirements of customers. Cloud workflow scheduling is a largely studied research area that targets efficient utilization of cloud resources. In this paper, we model a dynamic workflow scheduling problem, which is among the first attempts that incorporate dynamism on resource failures and changing number of objectives. A set of dynamic multi-objective evolutionary algorithms from the literature are utilized for dynamic workflow scheduling problem, where four of them are variants of the commonly used NSGA-II algorithm (DNSGA-II-HM, DNSGA-II-A, DNSGA-II-B, DNSGA-II-RI) and the remaining one is dynamic extension of the multi-objective particle swarm optimization algorithm (DMOPSO). In our experimental study, five different objectives are considered, which are minimization of the makespan, the cost and the energy, and maximization of the reliability and the resource utilization. The empirical study of the given five algorithms is conducted with real-world applications from Pegasus workflow management systems, where the DNSGA-II-B procedure outperforms the other alternatives for most of the test instances, based on the number of non-dominated solutions, the Schott's spacing and the hyper-volume metrics.

关键词： Workflow scheduling resource failures dynamic multi-objective evolutionary algorithms

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Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 307-322页

作者： Ismayilov, Goshgar Topcuoglu, Haluk Rahmi Marmara Univ Comp Engn Dept TR-34722 Istanbul Turkey

Workflow scheduling is a largely studied research topic in cloud computing, which targets to utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In this paper, we model dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP) where the source of dynamism is based on both resource failures and the number of objectives which may change over time. Software faults and/or hardware faults may cause the first type of dynamism. On the other hand, confronting real-life scenarios in cloud computing may change number of objectives at runtime during the execution of a workflow. In this study, we propose a prediction based dynamic multi-objective evolutionary algorithm, called NN-DNSGA-II algorithm, by incorporating artificial neural network with the NSGA-II algorithm. Additionally, five leading non-prediction based dynamic algorithms from the literature are adapted for the dynamic workflow scheduling problem. Scheduling solutions are found by the consideration of six objectives: minimization of makespan, cost, energy and degree of imbalance;and maximization of reliability and utilization. The empirical study based on real-world applications from Pegasus workflow management system reveals that our NN-DNSGA-II algorithm significantly outperforms the other alternatives in most cases with respect to metrics used for DMOPs with unknown true Pareto-optimal front, including the number of non-dominated solutions, Schott's spacing and Hypervolume indicator. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Workflow scheduling Resource failures Changing number of objectives dynamic multi-objective evolutionary algorithms Neural networks

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A Type Detection Based dynamic multi-objective evolutionary Algorithm 21st

A Type Detection Based Dynamic Multi-objective Evolutionary ...

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21st International Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Comp Engn Dept TR-34722 Istanbul Turkey

ISBN: (纸本)9783319775388

Characterization of dynamism is an important issue for utilizing or tailoring of several dynamic multi-objective evolutionary algorithms (DMOEAs). One such characterization is the change detection, which is based on proposing explicit schemes to detect the points in time when a change occurs. Additionally, detecting severity of change and incorporating with the DMOEAs is another attempt of characterization, where there is only a few related works presented in the literature. In this paper, we propose a type-detection mechanism for dynamic multi-objective optimization problems, which is one of the first attempts that investigate the significance of type detection on the performance of DMOEAs. Additionally, a hybrid technique is proposed which incorporates our type detection mechanism with a given DOMEA. We present an empirical evaluation by using seven test problems from all four types and five performance metrics, which clearly validate the motivation of type detection as well as significance of our hybrid technique.

关键词： dynamic multi-objective Optimization Problems Non-dominated Sorting Genetic Algorithm (NSGA-II) Type detection dynamic multi-objective evolutionary algorithms

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Memory-Assisted dynamic multi-objective evolutionary Algorithm for Feature Drift Problem

Memory-Assisted Dynamic Multi-Objective Evolutionary Algorit...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Fatih Sultan Mehmet Vakif Univ Comp Engn Dept Istanbul Turkey Marmara Univ Comp Engn Dept Istanbul Turkey

ISBN: (纸本)9781728169293

In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of relevant features after the occurrence of every change in the environment. An artificial neural network is employed to classify the new instances based on the up-to-date obtained set of relevant features efficiently. Our algorithm exploits a detection mechanism for the severity of changes to estimate the severity level of occurred changes and adaptively replies to these changes by introducing diversity to algorithm solutions. Furthermore, a fixed-size memory is used to store the good solutions and reuse them after each change to accelerate the convergence and searching process of the algorithm. The experimental results using three datasets and different environmental parameters show that the combination of our improved feature selection algorithm with the artificial neural network outperforms related work.

关键词： dynamic multi-objective evolutionary algorithms learning in non-stationary environments severity of changes feature drift memory-based algorithms

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