A modular and efficient hardware architecture for particle swarm optimization algorithm

Particle Swarm Optimization (PSO), a population based optimization algorithm, has recently been attracting the attention of the embedded computing community. It is an efficient tool for many continuous multimodal and multidimensional problem classes. This paper first evaluates the performance of the PSO algorithm on embedded processor platforms with limited computational resources. The results on such platforms demonstrate the lack of sufficient execution speed for real-time applications. Thus, to address the shortcomings of the software PSO we developed a hardware architecture that significantly accelerates its execution performance. Besides improving the execution efficiency, the design is shown to be modular, flexible and reusable for solving different optimization problems. The accelerated execution performance of the proposed architecture is demonstrated on standard mathematical benchmark functions as well as on a real world problem scenario: emission source localization in distributed sensor networks. A parallelization scheme for further speed-up of the hardware PSO is also demonstrated.

[1]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[2]  Hamid Haj Seyyed Javadi,et al.  Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[3]  Byung-Il Koh,et al.  Parallel asynchronous particle swarm optimization , 2006, International journal for numerical methods in engineering.

[4]  Richard E. Haskell,et al.  Accelerating the performance of particle swarm optimization for embedded applications , 2009, 2009 IEEE Congress on Evolutionary Computation.

[5]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[6]  Richard E. Haskell,et al.  Multi-swarm parallel PSO: Hardware implementation , 2009, 2009 IEEE Swarm Intelligence Symposium.

[7]  D.M. Hanna,et al.  Particle Swarm Optimization for classification of breast cancer data using single and multisurface methods of data separation , 2007, 2007 IEEE International Conference on Electro/Information Technology.

[8]  T. E. Hull,et al.  Random Number Generators , 1962 .

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

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

[11]  Richard E. Haskell,et al.  Hardware PSO for sensor network applications , 2008, 2008 IEEE Swarm Intelligence Symposium.

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Peter Martin,et al.  An Analysis Of Random Number Generators For A Hardware Implementation Of Genetic Programming Using FPGAs And Handel-C , 2002, GECCO.

[14]  Andres Upegui,et al.  A Population-oriented Architecture for Particle Swarms , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[15]  Robert J. Marks,et al.  FPGA implementation of particle swarm optimization for inversion of large neural networks , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

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

[17]  Jaroslaw Sobieszczanski-Sobieski,et al.  A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations , 2005 .

[18]  B J Fregly,et al.  Parallel global optimization with the particle swarm algorithm , 2004, International journal for numerical methods in engineering.

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

[20]  Yizhen Zhang,et al.  Particle swarm optimization for unsupervised robotic learning , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[21]  Christian Posthoff,et al.  Earthquake classifying neural networks trained with random dynamic neighborhood PSOs , 2007, GECCO '07.

[22]  Thomas Stützle,et al.  A Comparison of Particle Swarm Optimization Algorithms Based on Run-Length Distributions , 2006, ANTS Workshop.

[23]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[24]  Richard E. Haskell,et al.  Particle Swarm Optimization for Emission Source Localization in Sensor Networks , 2009 .

[25]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[26]  Andres Upegui,et al.  Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[27]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[28]  Amin Farmahini Farahani,et al.  SOPC-Based Architecture for Discrete Particle Swarm Optimization , 2007, 2007 14th IEEE International Conference on Electronics, Circuits and Systems.

[29]  Amin Farmahini Farahani,et al.  Scalable Architecture for on-Chip Neural Network Training using Swarm Intelligence , 2008, 2008 Design, Automation and Test in Europe.

[30]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[31]  Gabriella Kókai,et al.  Using Hardware-Based Particle Swarm Method for Dynamic Optimization of Adaptive Array Antennas , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[32]  Thomas E. Potok,et al.  Document clustering using particle swarm optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[33]  Gisbert Schneider,et al.  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training , 2006, BMC Bioinformatics.

[34]  Darrin M. Hanna,et al.  Particle Swarm Optimization for Cluster-Based Classification of Breast Cancer Data , 2007, GEM.

[35]  Tim Blackwell,et al.  Understanding particle swarms through simplification: a study of recombinant PSO , 2007, GECCO '07.

[36]  Thomas Stützle,et al.  Ant Colony Optimization and Swarm Intelligence , 2008 .

[37]  Bo Yang,et al.  Survey on Applications of Particle Swarm Optimization in Electric Power Systems , 2007, 2007 IEEE International Conference on Control and Automation.

[38]  Yutaka Maeda,et al.  Simultaneous Perturbation Particle Swarm Optimization Using FPGA , 2007, 2007 International Joint Conference on Neural Networks.

[39]  Jianming Zhang,et al.  Optimization design based on PSO algorithm for PID controller , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[40]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.