A novel hybrid approach for feature selection in software product lines

被引：1

作者：

Yadav, Hitesh ^{[1
]}

Chhikara, Rita ^{[1
]}

Kumari, A. Charan ^{[2
]}

机构：

[1] NorthCap Univ, Gurugram, Haryana, India

[2] Dayalbagh Educ Inst, Gurugram, Haryana, India

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2021年 / 80卷 / 04期

关键词：

Particle swarm optimization; Hyper-heuristic; Biogeography-based optimization; Firefly; Genetic algorithm (GA); Bird swarm optimization (BSA); Software product lines (SPL); Feature model (FM); BIOGEOGRAPHY-BASED OPTIMIZATION; PARTICLE SWARM; ALGORITHM; PSO;

D O I：

10.1007/s11042-020-09956-6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Software Product Line (SPL) customizes software by combining various existing features of the software with multiple variants. The main challenge is selecting valid features considering the constraints of the feature model. To solve this challenge, a hybrid approach is proposed to optimize the feature selection problem in software product lines. The Hybrid approach 'Hyper-PSOBBO' is a combination of Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO) and hyper-heuristic algorithms. The proposed algorithm has been compared with Bird Swarm Algorithm (BSA), PSO, BBO, Firefly, Genetic Algorithm (GA) and Hyper-heuristic. All these algorithms are performed in a set of 10 feature models that vary from a small set of 100 to a high-quality data set of 5000. The detailed empirical analysis in terms of performance has been carried out on these feature models. The results of the study indicate that the performance of the proposed method is higher to other state-of-the-art algorithms.

引用

页码：4919 / 4942

页数：24

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