A Hybrid ACO Algorithm for the Next Release Problem
In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO) for Next Release Problem (NRP). NRP, a NP-hard problem in requirement engineering, is to balance customer requests, resource constraints, and requirement dependencies by requirement selection. Inspired by the successes of Ant Colony Optimization algorithms (ACO) for solving NP-hard problems, we design our HACO to approximately solve NRP. Similar to traditional ACO algorithms, multiple artificial ants are employed to construct new solutions. During the solution construction phase, both pheromone trails and neighborhood information will be taken to determine the choices of every ant. In addition, a local search (first found hill climbing) is incorporated into HACO to improve the solution quality. Extensively wide experiments on typical NRP test instances show that HACO outperforms the existing algorithms (GRASP and simulated annealing) in terms of both solution quality and running time.
next release problem (NRP) ant colony optimization local search requirment engin eering
He Jiang Jingyuan Zhang Jifeng Xuan Zhilei Ren Yan Hu
School of Software Dalian University of Technology Dalian 116621. China School of Mathematical Sciences Dalian University of Technology Dalian 116024. China
国际会议
成都
英文
76-81
2010-06-23（万方平台首次上网日期，不代表论文的发表时间）