Real-Time Particle Swarm Optimization on FPGA for the Optimal Message-Chain Structure

Abstract

This paper addresses the real-time optimization problem of the message-chain structure to maximize the throughput in data communications based on half-duplex command-response protocols. This paper proposes a new variant of the particle swarm optimization (PSO) algorithm to resolve real-time optimization, which is implemented on field programmable gate arrays (FPGA) to be performed faster in parallel and to avoid the delays caused by other tasks on a central processing unit. The proposed method was verified by finding the optimal message-chain structure much faster than the original PSO, as well as reliably with different system and algorithm parameters.

Real-Time Monte Carlo Optimization on FPGA for the Efficient and Reliable Message Chain Structure

“…The overall process of formulating the system constraints was similar to our previous work [9]. But, in this work, t ISR and t PD were regarded as Gaussian random variables, T ISR and T PD , in order to include the uncertainty of the processing times for the ISR and received data in the RT, respectively.…”

“…Therefore, we have approached the real-time optimization problem in the context of the hardware acceleration using FPGA. In our previous work [9], the real-time optimization problem was tackled by the real-time variant of the particle swarm optimization (PSO) [10] algorithm which was accurately conducted within a consistent time. However, we found that it needs to be improved in the following three contexts.…”

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

“…In PSO [17,18], the trajectory of each particle is based on its flight experience and search space to update the speed of each particle. The position and speed vector of the ith particle are represented as c are acceleration coefficients.…”

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

“…In addition, other proposals use hardware accelerators. For example, in [17], the PSO algorithm is accelerated using FPGAs and in [18], the Jaya algorithm is accelerated through the use of GPUs.…”

“…Algorithm 7 shows the parallel learner phase for the SPP_ParTLBO algorithm. As can be seen, after identifying the best individual (i.e., the teacher) the thread that stored it in its subpopulation copies it into the global memory, so all the threads use the same teacher (lines [11][12][13][14][15][16][17].…”