# A Hybrid ACO Algorithm for the Next Release Problem

arXiv: https://arxiv.org/abs/1704.04777 · HTML: https://mlsanity.com/paper/1704.04777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [The application of artificial intelligence in software engineering: a review challenging conventional wisdom](https://mlsanity.com/paper/2108.01591/) (2021)
- [Structural Induced Exploration for Balanced and Scalable Multi-Robot Path Planning](https://mlsanity.com/paper/2512.21654/) (2025)
- [Hybrid ACO-CI Algorithm for Beam Design problems](https://mlsanity.com/paper/2303.16908/) (2023)

## Abstract (arXiv)

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.