Evolving Systems

, Volume 8, Issue 3, pp 221–231 | Cite as

Improved discrete particle swarm optimization for solving the practical sensors deployment

Original Paper


Sensors deployment has played an important role in many engineering applications, and the key goal is aimed at achieving an optimal surveillance region with a set of sensors. In this paper, a probabilistic strategy was chosen as the sensing model and a Gaussian probability distribution was employed, furthermore an accumulative probability for all the utilized sensors was presented and an optimal deployment on meshed planar grid was proposed. It was proved that the deployment problem was NP-complete, and an approach for approximating this solution should be resorted to intelligent methods. Particle swarm optimization (PSO) was a widely used artificial intelligent tool, and hereby an improved discrete PSO (DPSO) was proposed for solving the deployment problem, and which was based on integer coding, and the initialization, positions and velocities updating were distinct with the traditional PSO. In final, the deployment was investigated respectively by using uniform sensors (binary coding problem) and combinational sensors (multivariate integer coding problem), which were indicated to the core structure of proposed DPSO.


Sensors deployment Probabilistic sensing model Discrete particle swarm optimization 



This research is completely supported by National Key Research and Development Program “Research on Vibration Control Technology for Established Industrial Building Structures”, which is sponsored by Ministry of Science and Technology of the P. R. China and the Grant No. is 2016YFC0701302; and it is also launched as preparation for the revising work of ‘Code for design of vibration isolation’ (national code of P. R. China). Team colleagues in China National Machinery Industry Corporation (SINOMACH) and Technology Research Center of Engineering Vibration Control (EVCC) in China IPPR International Engineering Co., Ltd (IPPR) are gratefully acknowledged.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.China National Machinery Industry CorporationBeijingPeople’s Republic of China
  2. 2.China IPPR International Engineering Co., LtdBeijingPeople’s Republic of China

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