A parallel whale optimization algorithm and its implementation on FPGA

Whale optimization Algorithm (WOA), as a novel nature-inspired swarm optimization algorithm, has demonstrated superior performance in solving optimization problems. However, the performance deteriorates when applied to large-scale complex problems due to rapidly increasing running time required for huge computational tasks. Based on interactions within population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. Field Programmable Gate Array (FPGA) is an acceleration device of high parallelism and programmability. Meanwhile, Open Computing Language (OpenCL) provides a general architecture for heterogeneous development. In this paper, an efficient implementation of parallel WOA on FPGA is proposed named FPWOA. Experiment studies are conducted by performing WOA on CPU and FPWOA on FPGA respectively to solve ten well known benchmark functions. Numerical results show that our approach achieves a favourable speedup while maintaining optimization performance.