BAT and Hybrid BAT Meta-Heuristic for Quality of Service-Based Web Service Selection

2017 ◽  
Vol 26 (1) ◽  
pp. 123-137 ◽  
Author(s):  
Prashanth Podili ◽  
K.K. Pattanaik ◽  
Prashanth Singh Rana
Keyword(s):  
Service Composition ◽  
Optimization Problems ◽  
Service Selection ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Multi Objective ◽  
Heuristic Techniques ◽  
Web Service Selection ◽  
Discrete Pso

AbstractEfficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of BAT and Hybrid BAT algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. Hybrid BAT is found to be efficient for service composition.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

scholarly journals Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

2020 ◽  
Author(s):  
Tomohiro Harada ◽  
Misaki Kaidan ◽  
Ruck Thawonmas
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computing Time ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evaluation Time ◽  
Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

Quality-of-Service Based Web Service Composition and Execution Framework

2014 ◽  
pp. 74-93
Author(s):  
Bassam Al Shargabi ◽  
Osama Al-haj Hassan ◽  
Alia Sabri ◽  
Asim El Sheikh
Keyword(s):  
Quality Of Service ◽  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Service Selection ◽  
Web Service Composition ◽  
Web Service Selection ◽  
Selection Decisions ◽  
Uddi Registry

Software is gradually becoming more built by composing web services to support enterprise applications integration; thus, making the process of composing web services a significant topic. The Quality of Service (QoS) in web service composition plays a crucial role. As such, it is important to guarantee, monitor, and enforce QoS and ability to handle failures during execution. Therefore, an urgent need exists for a dynamic Web Service Composition and Execution (WSCE) framework based on QoS constraints. A WSCE broker is designed to maintain the following function: intelligent web service selection decisions based on local QoS for individual web service or global QoS based selection for composed web services, execution tracking, and adaptation. A QoS certifier controlled by the UDDI registry is proposed to verify the claimed QoS attributes. The authors evaluate the composition plan along with performance time analysis.

WS-SIM: A Toolkit for Modeling and Simulation of Service Composition

2013 ◽  
Vol 756-759 ◽  
pp. 1304-1308
Author(s):  
Fan Zhang
Keyword(s):  
Web Service ◽  
Service Composition ◽  
Simulation Experiment ◽  
Service Selection ◽  
Selection Algorithm ◽  
Web Service Selection ◽  
Composite Services ◽  
Selection Algorithms

Web service has been rapidly developed in recent years. Web service selection is an important issue in web service composition and lots of service selection algorithms have been presented. As lots of them select an atomic service during runtime, it is not an easy task to evaluate the quality of the composited service which composed of several atomic services. In this work we introduce WS-SIM, a simulation toolkit, to solve this problem. WS-SIM supports modeling and simulating composite services and atomic services in the real world. This system also provides many common service selection algorithms and researchers can custom their own service selection algorithm for a simulation experiment. The quality of composite services can also be generated by our system. Furthermore, to demonstrate suitability of the WS-SIM, in this paper, functionalities of our system are illustrated by a case study. This confirms the usability and the applicability of WS-SIM.

scholarly journals Effect of the Profile of the Decision Maker in the Search for Solutions in the Decision-Making Process

2021 ◽  
Vol 26 (2) ◽  
pp. 28
Author(s):  
Mercedes Perez-Villafuerte ◽  
Laura Cruz-Reyes ◽  
Nelson Rangel-Valdez ◽  
Claudia Gomez-Santillan ◽  
Héctor Fraire-Huacuja
Keyword(s):  
Optimization Problems ◽  
Region Of Interest ◽  
Multi Objective Optimization ◽  
Ordinal Classification ◽  
Multi Objective ◽  
Portfolio Problem ◽  
Real Size ◽  
Early Phases ◽  
Incorporation Method

Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classification are still scarce. In addition, preferences are rarely associated with a DM’s characteristics; the preference selection is arbitrary. This paper proposes a new hybrid multi-objective optimization algorithm called P-HMCSGA (preference hybrid multi-criteria sorting genetic algorithm) that allows the DM’s preferences to be incorporated in the optimization process’ early phases and updated into the search process. P-HMCSGA incorporates preferences using a multi-criteria ordinal classification to distinguish solutions as good and bad; its parameters are determined with a preference disaggregation method. The main feature of P-HMCSGA is the new method proposed to associate preferences with the characterization profile of a DM and its integration with ordinal classification. This increases the selective pressure towards the desired region of interest more in agreement with the DM’s preferences specified in realistic profiles. The method is illustrated by solving real-size multi-objective PPPs (project portfolio problem). The experimentation aims to answer three questions: (i) To what extent does allowing the DM to express their preferences through a characterization profile impact the quality of the solution obtained in the optimization? (ii) How sensible is the proposal to different profiles? (iii) How much does the level of robustness of a profile impact the quality of final solutions (this question is related with the knowledge level that a DM has about his/her preferences)? Concluding, the proposal fulfills several desirable characteristics of a preferences incorporation method concerning these questions.

Quality-of-Service Based Web Service Composition and Execution Framework

2011 ◽  
Vol 6 (3) ◽  
pp. 57-74 ◽  
Author(s):  
Bassam Al Shargabi ◽  
Osama Al-haj Hassan ◽  
Alia Sabri ◽  
Asim El Sheikh
Keyword(s):  
Quality Of Service ◽  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Service Selection ◽  
Web Service Composition ◽  
Web Service Selection ◽  
Selection Decisions ◽  
Uddi Registry

Software is gradually becoming more built by composing web services to support enterprise applications integration; thus, making the process of composing web services a significant topic. The Quality of Service (QoS) in web service composition plays a crucial role. As such, it is important to guarantee, monitor, and enforce QoS and ability to handle failures during execution. Therefore, an urgent need exists for a dynamic Web Service Composition and Execution (WSCE) framework based on QoS constraints. A WSCE broker is designed to maintain the following function: intelligent web service selection decisions based on local QoS for individual web service or global QoS based selection for composed web services, execution tracking, and adaptation. A QoS certifier controlled by the UDDI registry is proposed to verify the claimed QoS attributes. The authors evaluate the composition plan along with performance time analysis.

An Agent Architecture for Qos-Based Web Service Composition Using the Skyline Algorithm

2017 ◽  
Vol 6 (3) ◽  
pp. 179 ◽  
Author(s):  
El-Alami Ayoub ◽  
Hair Abdellatif
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Service Selection ◽  
Web Service Composition ◽  
Web Service Selection ◽  
Concrete Service ◽  
The Web ◽  
Selection Of

<p>Web service composition is a concept based on the built of an abstract process, by combining multiple existing class instances, where during the execution, each service class is replaced by a concrete service, selected from several web service candidates. This approach has as an advantage generating flexible and low coupling applications, based on its conception on many elementary modules available on the web. The process of service selection during the composition is based on several axes, one of these axes is the QoS-based web service selection. The Qos or Quality of Service represent a set of parameters that characterize the non-functional web service aspect (execution time, cost, etc...). The composition of web services based on Qos, is the process which allows the selection of the web services that fulfill the user need, based on its qualities. Selected services should optimize the global QoS of the composed process, while satisfying all the constraints specified by the client in all QoS parameters. In this paper, we propose an approach based on the concept of agent system and Skyline approach to effectively select services for composition, and reducing the number of candidate services to be generated and considered in treatment. To evaluate our approach experimentally, we use a several random datasets of services with random values of qualities.</p>

scholarly journals CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13765-13766
Author(s):  
Li Chen ◽  
Hua Xu
Keyword(s):  
Information Transfer ◽  
Optimization Problems ◽  
Optimization Process ◽  
Transfer Model ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Essential Characteristic ◽  
Multi Objective ◽  
Highly Correlated

One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.

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