By Nadia Nedjah
Single and Multi-Objective Evolutionary Computation (MOEA), Genetic Algorithms (GAs), man made Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) have gotten omnipresent in virtually each clever method layout. regrettably, the appliance of nearly all of those strategies is complicated and so calls for a massive computational attempt to yield worthwhile and useful effects. hence, devoted for evolutionary, neural and fuzzy computation is a key factor for designers. With the unfold of reconfigurable reminiscent of FPGAs, electronic in addition to analog implementations of such computation develop into within your budget. the belief at the back of this ebook is to supply quite a few designs for smooth computing recommendations that may be embedded in any ultimate product. additionally, to introduce the winning software of sentimental computing strategy to remedy many not easy difficulties encountered through the layout of embedded designs. Reconfigurable embedded designs for gasoline, ANNs, FCs and PSO are provided and evaluated. additionally, the appliance of quantum-based evolutionary computation and multi-objective evolutionary computation in addition to ACO are utilized to resolve demanding difficulties with regards to circuit synthesis, IP task, mapping and routing of purposes on Network-On-Chip infrastructures.
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Extra resources for Hardware for Soft Computing and Soft Computing for Hardware
Example text
The exchange of information between particles refers to the social behavior of the velocity definition. After upgrading the velocity and position of each particle it checks the stopping criteria and then displays the result or perform another operation. The value of each parameter of the PSO algorithm is crucial in the search process, and therefore the importance of defining appropriate values at the initialization step. The inertia weight w was introduced by [3] as a mechanism to control the exploration and exploitation abilities of the swarm.
The implementation was done using the High-Performance MPI (Message Passing Interface). Both synchronous and asynchronous solutions were investigated. The proposed implementation showed an improvement in processing time for a bio-mechanical test problem. An hardware/software co-design architecture to implement the PSO algorithm in an FPGA is reported in [7]. The particle accelerator module was implemented in hardware while the fitness function was kept in software. Whenever complex fitness functions, which impose high computational costs, this implementation showed poor performance as the critical point is the fitness function and not in the rest of the PSO algorithm.
47 Summary This chapter presents a parallel architecture of the PSO algorithm implemented as a hardware co-processor to the MicroBlaze processor. The FPGA used is a Xilinx Virtex 5 FPGA (xc5vfx70t). The architecture exploits the parallelism of updating the particle positions and velocities and getting the result of the fitness function independently of the others particles of the swarm. The synthesis results show that the scalability of the hardware depends on the number of particles used and the complexity of the fitness function.