Neural networks: applications in industry, business and science
Communications of the ACM
Evolutionary algorithms for the synthesis of embedded software
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Component selection and matching for IP-based design
Proceedings of the conference on Design, automation and test in Europe
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Handbook of Neural Computation
Handbook of Neural Computation
Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Parallel Hybrid Adventures with Simulated Annealing and Genetic Algorithms
ISPAN '02 Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A low power architecture for embedded perception
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Using Hardware-Based Particle Swarm Method for Dynamic Optimization of Adaptive Array Antennas
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
An Analysis Of PSO Hybrid Algorithms For Feed-Forward Neural Networks Training
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
A Population-oriented Architecture for Particle Swarms
AHS '07 Proceedings of the Second NASA/ESA Conference on Adaptive Hardware and Systems
Scalable architecture for on-chip neural network training using swarm intelligence
Proceedings of the conference on Design, automation and test in Europe
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Real-Time Neural Network Inversion on the SRC-6e Reconfigurable Computer
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
Hardware opposition-based PSO applied to mobile robot controllers
Engineering Applications of Artificial Intelligence
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This paper presents a novel hardware framework of particle swarm optimization (PSO) for various kinds of discrete optimization problems based on the system-on-a-programmable-chip (SOPC) concept. PSO is a new optimization algorithm with a growing field of applications. Nevertheless, similar to the other evolutionary algorithms, PSO is generally a computationally intensive method which suffers from long execution time. Hence, it is difficult to use PSO in real-time applications in which reaching a proper solution in a limited time is essential. SOPC offers a platform to effectively design flexible systems with a high degree of complexity. A hardware pipelined PSO (PPSO) Core is applied with which the required computational operations of the algorithm are performed. Embedded processors have also been employed to evaluate the fitness values by running programmed software codes. Applying the subparticle method brings the benefit of full scalability to the framework and makes it independent of the particle length. Therefore, more complex and larger problems can be addressed without modifying the architecture of the framework. To speed up the computations, the optimization architecture is implemented on a single chip master-slave multiprocessor structure. Moreover, the asynchronous model of PSO gains parallel efficacy and provides an approach to update particles continuously. Five benchmarks are exploited to evaluate the effectiveness and robustness of the system. The results indicate a speed-up of up to 98 times over the software implementation in the elapsed computation time. Besides, the PPSO Core has been employed for neural network training in an SOPC-based embedded system which approves the system applicability for real-world applications.