A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization.
- Algorithms,
- Artificial intelligence,
- Computation theory,
- Evolutionary algorithms,
- Fuzzy logic,
- Gallium,
- Genetic algorithms,
- Military applications,
- Network architecture,
- Optimal systems,
- Optimization,
- System of systems,
- Systems engineering,
- Engineering research,
- Architecting,
- MOEA,
- MOP,
- Multi objective evolutionary algorithms,
- Multi-objective optimization problem,
- SoS,
- Multiobjective optimization,
- GA,
- Meta-Architecture,
- Multi-Objective Evolutionary Algorithm,
- System-of-Systems
Available at: http://works.bepress.com/cihan-dagli/157/