Improved Particle Swarm Optimization Algorithm for Android Medical Care IOT using Modified Parameters

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

[1]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[2]  Oscar Castillo,et al.  Human evolutionary model: A new approach to optimization , 2007, Inf. Sci..

[3]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[4]  Tim Blackwell,et al.  A simplified recombinant PSO , 2008 .

[5]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[6]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Yu Liu,et al.  Center particle swarm optimization , 2007, Neurocomputing.

[8]  Tim M. Blackwell,et al.  The Lévy Particle Swarm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[10]  James Kennedy.  In Search of the Essential Particle Swarm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  Chih-Hung Wu,et al.  Particle Swarm Optimization-Aided Feature Selection for Spam Email Classification , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[12]  Wen-Tsai Sung,et al.  Design an Innovative Localization Engines into WSN via ZigBee and SOC , 2008 .

[13]  Zhao Xinchao.  A perturbed particle swarm algorithm for numerical optimization , 2010 .

[14]  Hancheng Xing,et al.  An Improved Gaussian Dynamic Particle Swarm Optimization Algorithm , 2006, 2006 International Conference on Computational Intelligence and Security.

[15]  Yanchun Liang,et al.  Hybrid evolutionary algorithms based on PSO and GA , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[16]  Marzuki Khalid,et al.  Function minimization in DNA sequence design based on continuous particle swarm optimization , 2009 .

[17]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[18]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[19]  Xinchao Zhao,et al.  A perturbed particle swarm algorithm for numerical optimization , 2010, Appl. Soft Comput..