ACO-based discrete and continuous optimisation algorithm for optimising multi-level truss topological design

Nwaki, Michael Menonye, Zhang, Shujun ORCID: 0000-0001-5699-2676, Bechkoum, Kamal ORCID: 0000-0001-5857-2763 and Liewe, David (2016) ACO-based discrete and continuous optimisation algorithm for optimising multi-level truss topological design. In: 5th international conference on Bionics Engineering ICBE 2016, 21st to 24th June 2016, Ningbo Campus, University of Nottingham, Ningbo, China.

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Abstract

Topological design truss problems are known to be difficult to solve to optimality, partly due to the inaccuracies of computational strategies to evaluate the impact of external forces acting on a specific edge (truss), which can have significant resultant effect on other trusses, through the individual vertices (node) and the magnitude of these resultant effects are difficult to accurately estimate due to the damping effect of the vertices (nodes). Besides, the largeness of the search space can be an issue, which can only be resolved with superior computational power and strategies. The complexities of these problems becomes exponentially larger when it involves multi-level hierarchies. This paper proposes a variant of ant-colony metaheuristic algorithm (ACO). The proposed algorithm includes a local search metaheuristic. In this algorithm, the ACO will be used exploitatively to identify high performance area of the search field, through the continuous interactions between the search agents (ants) and then using the local search algorithms, such as hill-climber to localise the search solutions. The feasibility of the structure of each combinatorial solutions, i.e. the discrete options and the corresponding continuous design variables will be evaluated using the Grubler’s criteria (degree of freedom), to determine the kinematic stability i.e. the statistical and dynamic balancing of a solution (structure). From the computer simulation results, it has been shown that the proposed algorithm can be used simultaneously, in searching across multi-level hierarchies for solving trust topological design problems with both the discrete and continuous design parameters within a given set of constraints. It is effective and more efficient than the existing algorithms such as GA for the similar problems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	ACO, optimisation algorithm multi-level-hierarchies, truss topological design
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	12 May 2016 15:54
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/3475

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