An Improved Evolutionary Multiobjective Service Composition Algorithm

Author

Hao Yin ; Changsheng Zhang ; Ying Guo ; Bin Zhang

Author_Institution

Coll. of Inf. & Eng., Northeastern Univ., Shenyang, China

Volume

1

fYear

2013

fDate

28-29 Oct. 2013

Firstpage

269

Lastpage

272

Abstract

Evolutionary multi-objective service composition optimizer (E³) is a recently proposed optimization framework for SLA-Aware service composition. It considers multiple SLAs simultaneously and produces a set of Pareto solutions. Two multi-objective genetic algorithms: E³-MOGA and Extreme-E³ provided by E³ have shown very good performance in comparison to NSGA-II. In this paper, an improved version of E³-MOGA, namely E³-IMOGA is proposed, which incorporates a fine-grained domination assignment value strategy. We evaluated our approach experimentally using dataset and compared with E³-MOGA and NSGA-II. It reveals promising results in terms of the quality of individuals and the time for finding all feasible individuals.

Keywords

Pareto optimisation; combinatorial mathematics; contracts; genetic algorithms; service-oriented architecture; E³-IMOGA; E³-MOGA; NSGA-II; Pareto solutions; SLA-aware service composition; extreme-E³ optimizer; fine-grained domination assignment value strategy; improved evolutionary multiobjective service composition algorithm; multiobjective genetic algorithms; service-level agreement; service-oriented architecture; Abstracts; Concrete; Gold; Optimization; Platinum; Quality of service; Throughput; domination value; optimization of service composition; quality of service; service-level agreement;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on

Conference_Location

Hangzhou

Type

conf

DOI

10.1109/ISCID.2013.74

Filename

6804987