Visible to the public Parameter selection for swarm intelligence algorithms \#x2014; Case study on parallel implementation of FSS

TitleParameter selection for swarm intelligence algorithms \#x2014; Case study on parallel implementation of FSS
Publication TypeConference Paper
Year of Publication2017
AuthorsMenezes, B. A. M., Wrede, F., Kuchen, H., Neto, F. B. de Lima
Conference Name2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
Keywordscomplex optimization problems, Complexity theory, composability, computational complexity, Computational Intelligence, Correlation, execution time, Fish, Fish School Search, Frequency selective surfaces, FSs, Iterative methods, number-of-iterations, optimisation, parallel implementation, parameter selection, population size, problem complexity, pubcrawl, search problems, SI algorithms, Silicon, Sociology, swarm intelligence, swarm intelligence algorithms

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.

Citation Keymenezes_parameter_2017