A Hybrid ACO Algorithm for the Next Release Problem

TL;DR

This paper introduces a Hybrid Ant Colony Optimization algorithm (HACO) for the Next Release Problem, effectively balancing customer requests, resources, and dependencies to improve solution quality and efficiency.

Contribution

The paper presents a novel hybrid ACO algorithm with local search for NRP, outperforming existing methods in solution quality and runtime.

Findings

HACO outperforms GRASP and simulated annealing in experiments.

HACO achieves better solution quality for NRP.

HACO reduces computation time compared to existing algorithms.

Abstract

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.