98 research outputs found
Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm
Web Service Composition (WSC) is a particularly promising application of Web
services, where multiple individual services with specific functionalities are
composed to accomplish a more complex task, which must fulfil functional
requirements and optimise Quality of Service (QoS) attributes, simultaneously.
Additionally, large quantities of data, produced by technological advances,
need to be exchanged between services. Data-intensive Web services, which
manipulate and deal with those data, are of great interest to implement
data-intensive processes, such as distributed Data-intensive Web Service
Composition (DWSC). Researchers have proposed Evolutionary Computing (EC)
fully-automated WSC techniques that meet all the above factors. Some of these
works employed Memetic Algorithms (MAs) to enhance the performance of EC
through increasing its exploitation ability of in searching neighbourhood area
of a solution. However, those works are not efficient or effective. This paper
proposes an MA-based approach to solving the problem of distributed DWSC in an
effective and efficient manner. In particular, we develop an MA that hybridises
EC with a flexible local search technique incorporating distance of services.
An evaluation using benchmark datasets is carried out, comparing existing
state-of-the-art methods. Results show that our proposed method has the highest
quality and an acceptable execution time overall.Comment: arXiv admin note: text overlap with arXiv:1901.0556
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics
In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)
An Energy Efficient Service Composition Mechanism Using a Hybrid Meta-heuristic Algorithm in a Mobile Cloud Environment
By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms
Qos-Aware Web Services Composition Using Grasp with Path Relinking
In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added
value logic as a service. When several alternative service providers are available for composition, quality of service
(QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the
creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NPhard
optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web
service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the
evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments
show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to
40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when
results must be available in 100ms; i.e. it provides compositions with a better QoS, implying cost savings, increased
availability and reduced execution times for the end-user.CICYT TIN2009-07366CICYT TIN2012-32273Junta de AndalucĂa P12-TIC-1867Junta de AndalucĂa TIC-590
A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS-ABC Algorithm
This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms
An Initial Value Calibration Method for the Wheel Force Transducer Based on Memetic Optimization Framework
Some initial values of the wheel force transducer (WFT) change after being mounted in the vehicle. The traditional static calibration is inadequate to fully obtain these initial values. Aiming to this problem, an online initial value calibration method is proposed. The method does not require any additional calibration equipment or manual operation and just requires the vehicle mounted with the WFT to be driven on a flat road with constant speed. In this way, an initial value mode is constructed and then converted to an optimization problem. To solve this problem and acquire the right initial value, an improved Memetic framework based on particle swarm optimization (PSO) and Levenberg-Marquardt (LM) is adopted. To verify the effect of the proposed method, the real WFT data is used and the comparative test is carried out. The experiment result shows that the proposed method is superior to the traditional one and can improve the measurement accuracy effectively
Automated Design of Metaheuristic Algorithms: A Survey
Metaheuristics have gained great success in academia and practice because
their search logic can be applied to any problem with available solution
representation, solution quality evaluation, and certain notions of locality.
Manually designing metaheuristic algorithms for solving a target problem is
criticized for being laborious, error-prone, and requiring intensive
specialized knowledge. This gives rise to increasing interest in automated
design of metaheuristic algorithms. With computing power to fully explore
potential design choices, the automated design could reach and even surpass
human-level design and could make high-performance algorithms accessible to a
much wider range of researchers and practitioners. This paper presents a broad
picture of automated design of metaheuristic algorithms, by conducting a survey
on the common grounds and representative techniques in terms of design space,
design strategies, performance evaluation strategies, and target problems in
this field
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