Meta-heuristics in multidimensional systems stability study

Multidimensional systems, or n-D systems, are systems having several independent variables. Several topics, in particular stability, of n-D systems (n > 1) have attracted the interest of many researchers. The main reason, is because the extension stability theory of 1-D systems to systems with higher dimensions is not straightforward. In this paper, two adopted meta-heuristics algorithms are used for complementing the study of systems stability based on their polynomial characteristics over the variables boundaries. The two meta-heuristics are genetic algorithm and particle swarm optimization due to its popularity. Practical results of both meta-heuristics are compared and the better algorithm highlighted. The results demonstrate that meta-heuristics can be applied in studding multidimensional system stability.