Quantum Inspired Evolutionary Algorithm for Optimizing Sensor Selection

M. Naeem, H.M. Ali, and D.C. Lee (Canada)


Sensor selection, convex optimization, Quantum Inspired Evolutionary Algorithm, combinatorial optimization


In this paper, we apply a Quantum Inspired Evolutionary Algorithm (QIEA) to the problem of optimally selecting k sensors from a set of m sensors placed for parameter estimation. The computational complexity of finding an optimal subset of sensor through exhaustive search can grow exponentially with the numbers (m and k) of sensors. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. We implemented a QIEA for solving the sensor selection problem and tested its performance through extensive computational experiments. We found that the performance of proposed QIEA-based sensor selection algorithm is better than well known sensor selection algorithms.

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