An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
Automated testing in software engineering: using ant colony and self-regulated swarms
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
The multi-objective next release problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Information Processing Letters
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Ant Colony Optimization for the Next Release Problem: A Comparative Study
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Interactive requirements prioritization using a genetic algorithm
Information and Software Technology
Controversy Corner: Search Based Software Engineering: Review and analysis of the field in Brazil
Journal of Systems and Software
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Ant Colony Optimization (ACO) has been successfully employed to tackle a variety of hard combinatorial optimization problems, including the traveling salesman problem, vehicle routing, sequential ordering and timetabling. ACO, as a swarm intelligence framework, mimics the indirect communication strategy employed by real ants mediated by pheromone trails. Among the several algorithms following the ACO general framework, the Ant Colony System (ACS) has obtained convincing results in a range of problems. In Software Engineering, the effective application of ACO has been very narrow, being restricted to a few sparse problems. This paper expands this applicability, by adapting the ACS algorithm to solve the well-known Software Release Planning problem in the presence of dependent requirements. The evaluation of the proposed approach is performed over 72 synthetic datasets and considered, besides ACO, the Genetic Algorithm and Simulated Annealing. Results are consistent to show the ability of the proposed ACO algorithm to generate more accurate solutions to the Software Release Planning problem when compared to Genetic Algorithm and Simulated Annealing.