Affiliations: [a] Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
| [b] Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
| [c] Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
Correspondence:
[*]
Corresponding author. Karamollah Bagherifard, Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran. E-mail: karam.bagherifard@gmail.com.
Abstract: Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works’ results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.
Keywords: Next release problem, multi-objective evolutionary algorithm, search-based software engineering, teaching-learning based optimization, non-repetitive population
