Parallel design of intelligent optimization algorithm based on FPGA

Abstract

Intelligent optimization algorithm (IOA) has been widely studied and applied to solve various optimization problems. When scholars improve IOA with mathematical methods, they also want to seek an effective method to implement algorithms with higher real time, especially for a complex problem. Parallel design is an effective method to improve the real time of IOA. Currently, the parallel programming based on open multi-processing (OpenMP) and compute unified device architecture (CUDA) are two popular methods. To find and develop a new IOA parallel method, in this paper, a parallel design and implementation method based on field programmable gate array (FPGA) is explored. In order to validate the proposed method, parallel genetic algorithm (GA) and parallel particle swarm optimization (PSO) algorithm are realized by the proposed method. Furthermore, the performance and advantage of the proposed FPGA-based parallel IOA method are tested by comparing with OpenMP-based parallel programming and CUDA-based parallel programming, the final results show that the proposed method with highest real-time performance in IOA parallel implementation. A case study by using FPGA-based parallel simulate annealing (SA) to address job shop scheduling problem (JSSP) to illustrate the proposed method has high potential in industrial applications.