On the influence of the swimming operators in the Fish School Search algorithm

Real world engineer problems sometimes involve high dimensional spaces. It makes them hard to compute. A common approach to tackle such challenges is to apply swarm based or evolutionary algorithms. Fish School Search (FSS) is one of such techniques that excels on difficult search problems. As FSS is a recent technique and only output results were investigated so far. This paper analyzes the influence of the FSS operators on the performance of the algorithm in six benchmark functions. We assessed the influence of each swimming operator separately. We found that the volitive mechanism is the operator that affords most of the exploration abilities during the search process. The carried out assessment has also shown that, in average, the best results are obtained only when all the FSS operators are actived. It means that all operators are fairly relevant and complementary. Moreover, we compared FSS results with some PSO variations and showed that FSS outperformed these PSO algorithms in some cases.