A Hardware Architecture of Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a useful algorithm to deal with non-linear problems such as route economic management optimization, vehicle routing optimization and so on. Several different kinds of improved PSO algorithms is provided to further increase its searching performance, which means PSO can deal with various kinds of situation through these improved algorithms. Moreover, Multi-Swarm strategy of PSO (MSPSO) is introduced to avoid premature and reach the optimal solution with less iteration time. However, software implementation of MSPSO is too time-consuming to be employed into real-time application when particles number and iterations time are huge, even on high-speed computer. Moreover, the synchronous hardware architecture of MSPSO is ineffective since it cannot achieve the maximum performance of each module during the calculation. In order to accelerate the processing speed of MSPSO, an asynchronous architecture of MSPSO based on Field-Programmable Gate Array (FPGA) is proposed in this research. The asynchronous architecture can improve the efficiency by executing the function of each module independently with maximum performance. In addition, Asynchronous Wrapper (AW) with handshaking protocol is adopted to connect core modules and peripheral modules, which can greatly enhance the stability of data exchange. The experimental results confirm that the asynchronous approach can drastically reduce the calculation time compared with synchronous approach.

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

[2]  Carlos H. Llanos,et al.  Hardware Particle Swarm Optimization with passive congregation for embedded applications , 2011, 2011 VII Southern Conference on Programmable Logic (SPL).

[3]  Wenbo Xu,et al.  Improving Quantum-Behaved Particle Swarm Optimization by Simulated Annealing , 2006, ICIC.

[4]  Carlos A. Coello Coello,et al.  Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer , 2004, GECCO.

[5]  Xiaodong Li,et al.  Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization , 2004, GECCO.

[6]  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).

[7]  Ching-Chang Wong,et al.  Hardware/software co-design for particle swarm optimization algorithm , 2010, SMC.

[8]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[9]  Yuji Tanabe,et al.  A Random Time-Varying Particle Swarm Optimization for the Real Time Location Systems , 2008 .

[10]  Leandro dos Santos Coelho,et al.  Hardware Particle Swarm Optimization Based on the Attractive-Repulsive Scheme for Embedded Applications , 2010, 2010 International Conference on Reconfigurable Computing and FPGAs.

[11]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

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

[13]  Guochu Chen,et al.  Two Sub-swarms Particle Swarm Optimization Algorithm , 2005, ICNC.

[14]  Peter Y. K. Cheung,et al.  Asynchronous wrapper for heterogeneous systems , 1997, Proceedings International Conference on Computer Design VLSI in Computers and Processors.

[15]  Caro Lucas,et al.  Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization , 2010, Eng. Appl. Artif. Intell..