  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.
