SPSO Parallelization Strategies for Electromagnetic Applications

Two parallelization techniques, GPGPU and Pthreads for multiprocessor architectures, are used to implement a SPSO algorithm in order to solve electromagnetic optimization problems. Several configurations for the GPGPU implementation are tested and a new full parallel minimum branching implementation is proposed. The best GPGPU approaches are then compared with a Pthreads implementation in terms of speed up and solution quality. To test the efficiency of the parallelization techniques two electromagnetic optimization problems were chosen, namely the TEAM22 benchmark and Loney’s solenoid. In the end the paper provides suggestions regarding what parallelization technique should be used considering the implementation features of the optimization function.

[1]  Iliana Castro Liera,et al.  Parallel particle swarm optimization using GPGPU , 2012 .

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

[3]  Ruppa K. Thulasiram,et al.  Collaborative multi-swarm PSO for task matching using graphics processing units , 2011, GECCO '11.

[4]  Daniel Ioan,et al.  Embedded stochastic-deterministic optimization method with accuracy control , 1999 .

[5]  Wen Tan,et al.  Multi-core based parallelized cooperative PSO with immunity for large scale optimization problem , 2014, Proceedings of 2014 International Conference on Cloud Computing and Internet of Things.

[6]  Feng Wang,et al.  The Research of PID Controller Tuning Based on Parallel Particle Swarm Optimization , 2013 .

[7]  Vincent Roberge,et al.  Comparison of Parallel Particle Swarm Optimizers for Graphical Processing Units and Multicore Processors , 2013, Int. J. Comput. Intell. Appl..

[8]  Ying Tan,et al.  GPU-based parallel particle swarm optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[9]  Jack J. Purdum,et al.  C programming guide , 1983 .

[10]  Zhenmao Chen,et al.  Fast simulation of ECT signal due to a conductive crack of arbitrary width , 2006, IEEE Transactions on Magnetics.

[11]  Yang Yan,et al.  An adaptive version of parallel MPSO with OpenMP for Uncapacitated Facility Location problem , 2008, 2008 Chinese Control and Decision Conference.

[12]  Mehmet Fatih Tasgetiren,et al.  A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem , 2008, Comput. Oper. Res..

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Weichung Wang,et al.  Accelerating parallel particle swarm optimization via GPU , 2012, Optim. Methods Softw..

[15]  Marcos A. C. Oliveira,et al.  Impact of the Random Number generator quality on particle swarm optimization algorithm running on graphic processor units , 2010, 2010 10th International Conference on Hybrid Intelligent Systems.

[16]  S.S. Udpa,et al.  Three-dimensional defect reconstruction from eddy-current NDE signals using a genetic local search algorithm , 2004, IEEE Transactions on Magnetics.

[17]  Gabriela Ciuprina,et al.  Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag , 2002 .

[18]  M. Tech Student,et al.  OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE , 2013 .

[19]  Fabio Daolio,et al.  Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture , 2011, Inf. Sci..

[20]  Xiaojun Wu,et al.  Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point , 2011, Appl. Math. Comput..

[21]  Fabio Daolio,et al.  GPU-Based Road Sign Detection Using Particle Swarm Optimization , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.