Fortify particle swam optimizer (PSO) with principal components analysis: A case study in improving bound-handling for optimizing high-dimensional and complex problems

It is reported that the absorbing bound-handling approach may paralyze PSO when it is applied to high dimensional and complex problems. In this study, we introduce principal components analysis (PCA) into PSO in order to remedy the problem caused by the absorbing bound-handling approach. The experiments on 100-D composition functions demonstrate the effectiveness of PCA. Furthermore, the strong influence of bound-handling on PSO is also evidently revealed by the results. The fact that none of the studied bound-handling methods excels on all of the benchmark functions highlights the necessity of developing more sophisticated and robust bound handling approaches that can facilitate the application of PSO on high-dimensional problems.