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 occurrin...
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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. multiobjectiveevolutionaryalgorithm 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.
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 cla...
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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 evolutionarymulti-objective optimization approaches in achieving a better approximation to the convex hull more stably and with a more uniformly distributed set of solutions.
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-contrad...
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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.
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 communic...
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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.
作者：
Ma, WenpingWu, YueYun, JieXidian 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
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 ...
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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.
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...
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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.
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 th...
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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.
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 consumpt...
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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 evolutionaryalgorithm (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.
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 even...
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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.
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 ...
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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 evolutionaryalgorithm 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-objectivealgorithms.
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