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检索条件"主题词=multi-objective evolutionary algorithm"

共 545 条记录，以下是381-390 订阅

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A Self-adaptation Framework for Dealing with the Complexities of Software Changes

A Self-adaptation Framework for Dealing with the Complexitie...

引用

2017 IEEE 8th International Conference on Software Engineering and Service Science

作者： Jiewen Wan Qingshan Li Lu Wang Liu He Yvjie Li Software Engineering Institute Xidian University

Software Self-adaption(SA) is a promising technology to reduce the cost of software maintenance. However, the complexities of software changes such as various and producing different effects, interrelated and occurring in an unpredictable context challenge the SA. The current methods may be insufficient to provide the required self-adaptation abilities to handle all the existent complexities of changes. Thus, this paper presents a self-adaptation framework which can provide a multi-agent system for self-adaptation control to equip software system with the required adaptation abilities. we employ the hybrid control mode and construct a two-layer MAPE control structure to deal with changes hierarchically. multiobjective evolutionary algorithm and Reinforcement Learning are applied to plan an adequate strategy for these changes. Finally, in order to validate the framework, we exemplify these ideas with a meta-Search system and confirm the required selfadaptive ability.

关键词： Self-adaptive systems Search-based software engineering multi-agent systems Reinforcement Learning multi-objective evolutionary algorithm

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A New evolutionary multi-objective algorithm for Convex Hull Maximization

A New Evolutionary Multi-objective Algorithm for Convex Hull...

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IEEE Congress on evolutionary Computation

作者： Wenjing Hong Guanzhou Lu Peng Yang Yong Wang Ke Tang USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications (UBRI) School of Computer Science and Technology University of Science and Technology of China Hefei China 230027 School of Information Science and Engineering Central South University Changsha China 410083

ISBN: (纸本)9781479974931

Many real-world problems often have several, usually conflicting objectives. Traditional multi-objective optimization problems (MOPs) usually search for the Pareto-optimal solutions for this predicament. A special class of MOPs, the convex hull maximization problems which prefer solutions on the convex hull, has posed a new challenge for existing approaches for solving traditional MOPs, as a solution on the Pareto front is not necessarily a good solution for convex hull maximization. In this work, the difference between traditional MOPs and the convex hull maximization problems is discussed and a new evolutionary Convex Hull Maximization algorithm (ECHMA) is proposed to solve the convex hull maximization problems. Specifically, a Convex Hull-based sorting with Convex Hull of Individual Minima (CH-CHIM-sorting) is introduced, as well as a novel selection scheme, Extreme Area Extract-based selection (EAE-selection). Experimental results show that ECHMA significantly outperforms the existing approaches for convex hull maximization and evolutionary multi-objective optimization approaches in achieving a better approximation to the convex hull more stably and with a more uniformly distributed set of solutions.

关键词： multi-objective optimization convex hull maximization multi-objective evolutionary algorithm

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Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives

Parallel Differential Evolution approach for Cloud workflow ...

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

作者： Balouek-Thomert, Daniel Bhattacharya, Arya K. Caron, Eddy Gadireddy, Karunakar Lefevre, Laurent NewGenerat SR Paris France Univ Lyon UCH Lyon 5668 INRIA ENS LyonUMR CNRSLIP LabINRIA Avalon Team Lyon France Mahindra Ecole Cent Sch Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509006229

The recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. multi-objective evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables - in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a multi-objective Differential Evolution method (NSDE-II) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan.

关键词： Parallel computing workflow placement cloud computing energy makespan middleware Differential Evolution multi-objective evolutionary algorithm Pareto front convergence

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Design of the High-sensitivity RFID Sensor Tag with MOEA/D-DE 21

Design of the High-sensitivity RFID Sensor Tag with MOEA/D-D...

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21st International Symposium on Antennas and Propagation (ISAP)

作者： Song, Xiaotian Wang, Gang He, Yuxing Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

ISBN: (纸本)9784885523137

The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is firstly applied to design a high-sensitivity RFID sensor tag with the consideration of its communication performance. For demonstration, an RFID temperature sensor tag is designed and tested. Both simulated and measured results show the designed sensor tag achieves a three times higher sensing sensitivity and a better communication performance than the one in the literature.

关键词： RFID sensor tag sensitivity temperature sensing multi-objective evolutionary algorithm

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multi-objective Population-Based Incremental Learning for Community Structure Detection

Multi-objective Population-Based Incremental Learning for Co...

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

作者： Ma, Wenping Wu, Yue Yun, Jie Xidian Univ Int Res Ctr Intelligent Percept & ComputatMinist Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

ISBN: (纸本)9781509006229

The community structure detection of complex networks has become a hot topic in the past several years. In this paper, a new discrete framework of population-based incremental learning for complex networks problem is proposed. Based on the proposed discrete framework, a novel multi-objective population-based incremental learning algorithm is proposed to solve community structure detection problem. The proposed algorithm combines population-based incremental learning with the multi-objective evolutionary algorithm based on decomposition, this makes the evolution get directionality and converge fast. In order to discourage premature convergence, a random perturbation operator is adopted. The proposed algorithm has two contradictory objective functions termed as negative ratio association and ratio cut, respectively. The community structure detection results are a set of tradeoff solutions by simultaneous optimizing these two contradictory objective functions. Each of these solutions corresponds to a network community structure at one hierarchical level. Experiments on both real-world and synthetic networks prove the effectiveness of the proposed algorithm.

关键词： community structure detection multi-objective evolutionary algorithm population based incremental learning random perturbation operator

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multi-objective Software Effort Estimation 16

Multi-objective Software Effort Estimation

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38th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Sarro, Federica Petrozziello, Alessio Harman, Mark UCL London England Univ Portsmouth Portsmouth Hants England

ISBN: (纸本)9781450339001

We introduce a bi-objective effort estimation algorithm that combines Con fidence Interval Analysis and assessment of Mean Absolute Error. We evaluate our proposed algorithm on three different alternative formulations, baseline comparators and current state-of-the-art effort estimators applied to five real-world datasets from the PROMISE repository, involving 724 different software projects in total. The results reveal that our algorithm outperforms the baseline, state-of-the-art and all three alternative formulations, statistically significantly (p < 0.001) and with large effect size (<(A)over cap>(12) >= 0.9) over all five datasets. We also provide evidence that our algorithm creates a new state-of-the-art, which lies within currently claimed industrial human-expert-based thresholds, thereby demonstrating that our findings have actionable conclusions for practicing software engineers.

关键词： Software effort estimation multi-objective evolutionary algorithm confidence interval estimates uncertainty

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objective Space Partitioning with a Novel Conflict Information Measure for Many-objective Optimization

Objective Space Partitioning with a Novel Conflict Informati...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Luo, Naili Luo, Jianping Li, Xia Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Commun & Informat Proc Shenzhen 518060 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

In this paper, we present a novel conflict information measure used for objective space partitioning in solving many-objective optimization problems. Obtained from the current Pareto front approximation to estimate the degree of conflict between objectives, the conflict information is simply evaluated by counting the occurrence of conflict (improvement vs deterioration) out of all decision making sample pairs. The proposed method is compared with the latest objective space partitioning based on Pearson correlation coefficient conflict information. The results show that the proposed method outperforms the comparison method on identifying the conflicting objectives.

关键词： Many-objective optimization multi-objective evolutionary algorithm objective space partitioning Conflict information

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Quality of Services based routing using evolutionary algorithms for Wireless Sensor Network 15

Quality of Services based routing using Evolutionary Algorit...

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15th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Hajjej, Faten Ejbali, Ridha Zaied, Mourad Res Lab Intelligent Machines REGIM Lab Sfax Tunisia

ISBN: (纸本)9781467387095

In this paper we invoke a new approach for the multi-objective routing problems in Wireless Sensor Networks (WSNs). Our approach improves more than one single Quality of Services (QoS) exigency such as energy consumption and delay. However, the classical routing protocols in conventional network optimize a single objective or QoS parameters. The proposed approach adapted a multi-objective evolutionary algorithm (MOEA), specifically, the improved Strength Pareto evolutionary algorithm (SPEA2) in order to improve the QoS in WSNs. Our simulation results show that the SPEA2 algorithms are efficient in solving routing problems and are capable of finding the Pareto optimal Set. Additionally, we demonstrate that this approach provides better trade-off solutions in comparison to the classical routing protocols.

关键词： Wireless Sensor Network multi-objective evolutionary algorithm SPEA2 Quality of Service

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Sensor Event Mining with Hybrid Ensemble Learning and evolutionary Feature Subset Selection Model 3

Sensor Event Mining with Hybrid Ensemble Learning and Evolut...

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IEEE International Conference on Big Data

作者： Mehdiyev, Nijat Krumeich, Julian Werth, Dirk Loos, Peter German Res Ctr Artificial Intelligence DFKI Inst Informat Syst IWi Saarbrucken Germany

ISBN: (纸本)9781479999255

Recent advancements in sensor technology offer opportunities to manage business processes in a proactive manner. To enable an effective and real-time monitoring, sensor data have to be treated and processed in an event processing manner. Complex Event Processing is an efficient technology that detects useful complex events by matching primitive sensor events using event patterns. Event patterns can be represented as templates that combine primitive events by temporal, logical, spatial and sequential correlations to detect more complex events. Identifying event patterns out of streaming data with a high data volume and velocity is a challenging task. In this paper, we propose an Ensemble Model consisting of a crisp and fuzzy rule based classifiers in order to derive decision rules as event patterns. Before implementing the ensemble classifier directly to the streaming data, we select the most influential feature subset using a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated using real data obtained from accelerometer sensors. Promising results with high accuracy and appropriate level of computational complexity were obtained and discussed.

关键词： multi-objective evolutionary algorithm feature subset selection rule induction complex event processing ensemble learning

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evolutionary algorithm with AHP Decision-making Method for Cloud Workflow Service Composition 7

Evolutionary Algorithm with AHP Decision-making Method for C...

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IEEE 7th International Conference on Cloud

作者： Zhang, Miao Liu, Li Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467395601

multiple Cloud services can be composed as a single service to fulfill the execution of large-scale workflow application. It faces trade-offs among several QoS metrics causing by users' preferences. Most existing works utilize multi-objective evolutionary algorithms to address this problem. However, it is hardly to express different users' preferences. This paper proposes an evolutionary algorithm for Cloud service composition, combining NSGA-II with Decision-Making method to calculate Crowding Distance with triangular fuzzy number based on AHP. The experimental results show that our approach could precisely capture users' preferences, and perform better in scalability than other general multi-objective algorithms.

关键词： Cloud computing Services Composition multi-objective evolutionary algorithm AHP

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