SILVEREYE – The implementation of particle swarm optimization algorithm in a design optimization tool

26Citations
Citations of this article
53Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Engineers and architects are now turning to use computational aids in order to analyze and solve complex design problems. Most of these problems can be handled by techniques that exploit Evolutionary Computation (EC). However existing EC techniques are slow [8] and hard to understand, thus disengaging the user. Swarm Intelligence (SI) relies on social interaction, of which humans have a natural understanding, as opposed to the more abstract concept of evolutionary change. The main aim of this research is to introduce a new solver Silvereye, which implements Particle Swarm Optimization (PSO) in the Grasshopper framework, as the algorithm is hypothesized to be fast and intuitive. The second objective is to test if SI is able to solve complex design problems faster than EC-based solvers. Experimental results on a complex, single-objective high-dimensional benchmark problem of roof geometry optimization provide statistically significant evidence of computational inexpensiveness of the introduced tool.

Cite

CITATION STYLE

APA

Cichocka, J. M., Migalska, A., Browne, W. N., & Rodriguez, E. (2017). SILVEREYE – The implementation of particle swarm optimization algorithm in a design optimization tool. In Communications in Computer and Information Science (Vol. 724, pp. 151–169). Springer Verlag. https://doi.org/10.1007/978-981-10-5197-5_9

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free