An Improved Evolutionary multiobjective service composition algorithm

被引：0
|
作者：
Yin, Hao [1 ]
Zhang, Changsheng [1 ]
Guo, Ying [1 ]
Zhang, Bin [1 ]
机构：
[1] Northeastern Univ, Coll Informat & Engn, Shenyang, Peoples R China
来源：
2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1 | 2013年
关键词：
optimization of service composition; quality of service; service-level agreement; domination value;
D O I：
10.1109/ISCID.2013.74
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Evolutionary multi-objective service composition optimizer (E-3) 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: E-3-MOGA and Extreme-E-3 provided by E-3 have shown very good performance in comparison to NSGA-II. In this paper, an improved version of E-3-MOGA, namely E-3-IMOGA, is proposed, which incorporates a fine-grained domination assignment value strategy. We evaluated our approach experimentally using dataset from [1] and compared with E-3-MOGA and NSGA-II. It reveals promising results in terms of the quality of individuals and the time for finding all feasible individuals.
引用
收藏
页码：269 / 272
页数：4
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