TY - GEN
T1 - Combining Constraint Solving with Different MOEAs for Configuring Large Software Product Lines
T2 - 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
AU - Yu, Huiqun
AU - Shi, Kai
AU - Guo, Jianmei
AU - Fan, Guisheng
AU - Yang, Xingguang
AU - Chen, Liqiong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based Evolutionary Algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.
AB - Multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based Evolutionary Algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.
KW - Constraint solving
KW - Multi-objective evolutionary algorithm
KW - Search-based software engineering
KW - Software product lines
UR - https://www.scopus.com/pages/publications/85055453374
U2 - 10.1109/COMPSAC.2018.00016
DO - 10.1109/COMPSAC.2018.00016
M3 - 会议稿件
AN - SCOPUS:85055453374
T3 - Proceedings - International Computer Software and Applications Conference
SP - 54
EP - 63
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Lung, Chung-Horng
A2 - Conte, Thomas
A2 - Liu, Ling
A2 - Akiyama, Toyokazu
A2 - Hasan, Kamrul
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Claycomb, William
A2 - Cimato, Stelvio
A2 - Yang, Ji-Jiang
A2 - Zhang, Zhiyong
A2 - Ahamed, Sheikh Iqbal
A2 - Reisman, Sorel
A2 - Demartini, Claudio
A2 - Nakamura, Motonori
PB - IEEE Computer Society
Y2 - 23 July 2018 through 27 July 2018
ER -