Ant Colony Optimization
Identifying "good" architectural design alternatives with multi-objective optimization strategies
Proceedings of the 28th international conference on Software engineering
Early quality prediction of component-based systems - A generic framework
Journal of Systems and Software
ACO vs EAs for solving a real-world frequency assignment problem in GSM networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Software deployment architecture and quality-of-service in pervasive environments
International workshop on Engineering of software services for pervasive environments: in conjunction with the 6th ESEC/FSE joint meeting
Ant Colony Optimization for Multi-Objective Optimization Problems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
A user-centric approach for improving a distributed software system's deployment architecture
A user-centric approach for improving a distributed software system's deployment architecture
ArcheOpterix: An extendable tool for architecture optimization of AADL models
MOMPES '09 Proceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software
Expert Systems with Applications: An International Journal
Solving multi-criteria optimization problems with population-based ACO
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
E-SCIENCEW '10 Proceedings of the 2010 Sixth IEEE International Conference on e-Science Workshops
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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The automotive deployment problem is a real-world constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car's chassis. Prior work has shown that evolutionary algorithms such as NSGA-II can produce good quality solutions to this problem. This paper presents a population-based ant colony optimisation (PACO) approach that uses a single pheromone memory structure and a range of local search operators. The PACO and prior NSGA-II are compared on two realistic problem instances. Results indicate that the PACO is generally competitive with NSGA-II and performs more effectively as problem complexity---size and number of objectives---is increased.