Impact of problem dimension on the execution time of parallel particle swarm optimization implementation

In this study, parallel particle swarm optimization algorithm has been investigated as regards the impact of the problem properties on the execution time. Two major factors affect the performance of parallel evolutionary algorithms: the population size and the problem dimension. In this study, five well-know benchmark functions have been applied with different dimensions. Then, these functions have been compared as regards the execution time. Finally, uniformly distributed population has been compared with the chaotic distributed population based on the dimension and population size from previous discussion.