Parameter Selection for Swarm Intelligence Algorithms: Case Study on Parallel Implementation of FSS
Menezes Breno, Wrede Fabian, Kuchen Herbert, Buarque Fernando
Swarm Intelligence (SI) algorithms, such as Fish School Search (FSS), are well known as useful tools that can be used to achieve a good solution in a reasonable amount of time for complex optimization problems. And when problems increase in size and complexity, some increase in population size or number of iterations might be needed in order to achieve a good solution. In extreme cases, the execution time can be huge and other approaches, such as parallel implementations, might help to reduce it. This paper investigates the relation and trade off involving these three aspects in SI algorithms, namely population size, number of iterations, and problem complexity. The results with a parallel implementations of FSS show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior of the results, i.e. increasing the population over a certain threshold only leads to slight improvements.
Parameter Selection; Swarm Intelligence; Fish School Search; Parallel Implementation; Computational Intelligence