Title :
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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location :
Hangzhou
DOI :
10.1109/ISCID.2013.74
