Implementation of Parallel Simplified Swarm Optimization in CUDA
This is an incremental improvement for researchers and practitioners in optimization computing, enabling faster swarm intelligence algorithms on personal computers with GPUs.
The paper tackled the lack of a GPU-based Simplified Swarm Optimization algorithm by proposing Parallel Simplified Swarm Optimization (PSSO) on CUDA, which reduced time complexity by an order of magnitude of N and eliminated resource preemption issues.
As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.