Evolutionary ACO Algorithms for Truss Optimization Problems

Gan, Buntara S. and Hara, Takahiro Hara and Han, Aylie and Alisjahbana, Sofia W. and As’ad, Sholihin (2017) Evolutionary ACO Algorithms for Truss Optimization Problems. Procedia Engineering, Vol171. pp. 1100-1107. ISSN 1877-7058

Preview

Text (pdf) 1-s2.0-S1877705817304770-main.pdf - Published Version Download (321kB) | Preview

Abstract

Over the last decade, researchers have proposed several ant colony optimisation algorithms to solve combinatorial problems. Ant Colony Optimisation (ACO) was introduced by Dorigo et al. in the early 1990s and is based on the behaviour of natural ant colonies, in particular the foraging behaviour of real ant species. The indirect communication of real ants in the colony uses pheromone trail lying on the path to find the shortest trail between their food source and the nest. Recently, Evolutionary ACO algorithms have been proposed to solve truss optimisation problems (EACO algorithms). This algorithm can solve truss size and topology problems, which makes EACO very attractive to solve non-combinatorial optimisation problems. Computational tests are described to show the effectiveness of the EACO.

Item Type:	Article
Uncontrolled Keywords:	Ant Colony OptimisationEvolutionary AlgorithmTruss StructureTopology OptimisationSize Optimisation.
Subjects:	Civil Engineering
Divisions:	Fakultas Teknik dan Ilmu Komputer > Program Studi Teknik Sipil
Depositing User:	Users 2 not found.
Date Deposited:	19 May 2017 04:06
Last Modified:	19 May 2017 04:06
URI:	https://repository.bakrie.ac.id/id/eprint/708

Actions (login required)

View Item