A regular expression grouping algorithm based on artificial fish school algorithm

In order to solve the problem of great memory usage when merging pattern sets into Deterministic Finite Automaton (DFA), an important method is that it divides n regular expressions into m groups in reasonable ways. Through combining minimum interactive rate grouping strategy with the advantage of global search abilities of optimization of artificial fish school algorithm (ASFA), one grouping algorithm based on improved artificial fish school(GRE-ASFA) is proposed in this paper. We redefine the distance, center, neighborhood of artificial fish. In addition, the behavior of prey about artificial fish is also improved for better finding global optimization grouping results. With the purpose of testing the performance of GRE-ASFA, regular expressions are picked up from snort and L7-filter as experimental data and Becchi open source regular expression matching engine tool is used as the experimental tool. The results of the experiment shows that this improved intelligent algorithm can obtain a better grouping result and fewer DFA states number than Heuristic algorithm and intelligent algorithm based on Becchi grouping strategy.