A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Journal of Computational Physics
A Geometric Transversal Approach to Analyzing Track Coverage in Sensor Networks
IEEE Transactions on Computers
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
International Journal of Knowledge-based and Intelligent Engineering Systems
Multicriteria decision making (MCDM): a framework for research and applications
IEEE Computational Intelligence Magazine
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
Probabilistic track coverage in cooperative sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of takagi-sugeno fuzzy systems based on multi-objective genetic algorithms
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
A decision support system for the optimal deployment of drifting acoustic sensor networks for cooperative track detection in underwater surveillance applications is proposed and tested on a simulated scenario. The system integrates sea water current forecasts, sensor range models and simple drifting buoy kinematic models to predict sensor positions and temporal network performance. A multi-objective genetic optimization algorithm is used for searching a set of Pareto optimal deployment solutions (i.e. the initial position of drifting sonobuoys of the network) by simultaneously optimizing two quality of service metrics: the temporal mean of the network area coverage and the tracking coverage. The solutions found after optimization, which represent different efficient tradeoffs between the two metrics, can be conveniently evaluated by the mission planner in order to choose the solution with the desired compromise between the two conflicting objectives. Sensitivity analysis through the Unscented Transform is also performed in order to test the robustness of the solutions with respect to network parameters and environmental uncertainty. Results on a simulated scenario making use of real probabilistic sea water current forecasts are provided showing the effectiveness of the proposed approach. Future work is envisioned to make the tool fully operational and ready to use in real scenarios.