A Hybrid ACO Algorithm for the Next Release Problem
This work addresses the Next Release Problem for software requirement engineering, but it is incremental as it builds on existing Ant Colony Optimization methods.
The authors tackled the NP-hard Next Release Problem in requirement engineering by proposing a Hybrid Ant Colony Optimization algorithm (HACO) that incorporates local search, which outperformed existing methods like GRASP and simulated annealing in solution quality and running time on typical test instances.
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 uality and running time.