Authors:
Nikhil Padhye
1
;
Long Zuo
2
;
Chilukurii K. Mohan
2
and
Pramod K. Varshney
2
Affiliations:
1
Indian Institute of Technology Kanpur, India
;
2
Syracuse University, United States
Keyword(s):
Genetic algorithms, Multi-objective optimization, PCRLB, Sensor networks, Target tracking.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
When large sensor networks are applied to the task of target tracking, it is necessary to successively identify subsets of sensors that are most useful at each time instant. Such a task involves simultaneously maximizing target detection accuracy and minimizing querying cost, addressed in this paper by the application of multi-objective evolutionary algorithms (MOEAs). NSGA-II, a well-known MOEA, is demonstrated to be successful in obtaining diverse solutions (trade-off points), when compared to a ”weighted sum” approach that combines both objectives into a single cost function. We also explore an improvement, LS-DNSGA, which incorporates periodic local search into the algorithm, and outperforms standard NSGA-II on the sensor selection problem.