A reformulation of the ant colony optimization algorithm for large scale structural optimization

This study intends to improve performance of ant colony optimization (ACO) method for structural optimization problems particularly with many design variables or when design variables are chosen from large discrete sets. The algorithm developed with ACO method employs the so-called pheromone scaling approach to overcome entrapment of the search in a poor local optimum and thus to recover efficiency of the method for large-scale optimization problems. Besides, a new formulation is proposed for the local update parameter in the algorithm. The efficacy of the proposed algorithm is quantified using two numerical design examples chosen from practical size optimum design of steel structures. The results obtained with the proposed algorithm are compared with those of other methods, such as particle swarm optimization (PSO), harmony search optimization (HSO) and genetic algorithms (GAs). The design problems are formulated according to the provisions of ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution).


Ant Colony Search Method in Practical Structural Optimization
Hasançebi, Oğuzhan (2011-06-01)
This paper is concerned with application and evaluation of ant colony optimization (ACO) method to practical structural optimization problems. In particular, a size optimum design of pin-jointed truss structures is considered with ACO such that the members are chosen from ready sections for minimum weight design. The application of the algorithm is demonstrated using two design examples with practical design considerations. Both examples are formulated according to provisions of ASD-AISC (Allowable Stress D...
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Hasançebi, Oğuzhan (2012-11-01)
In the present study an enhanced variant of the big bang-big crunch (BB-BC) technique, namely exponential BB-BC algorithm (EBB-BC) is developed for code based design optimization of steel frame structures. It is shown that the standard version of the BB-BC algorithm is sometimes unable to produce reasonable solutions to problems from discrete design optimization of steel frames. Hence, through investigating the shortcomings of BB-BC algorithm, it is aimed to reinforce the performance of the technique for th...
Improving the performance of simulated annealing in structural optimization
Hasançebi, Oğuzhan; Saka, Mehmet Polat (2010-03-01)
This study aims at improving the performance of simulated annealing (SA) search technique in real-size structural optimization applications with practical design considerations. It is noted that a standard SA algorithm usually fails to produce acceptable solutions to such problems associated with its poor convergence characteristics and incongruity with theoretical considerations. In the paper novel approaches are developed and incorporated into the standard SA algorithm to eliminate the observed drawbacks ...
Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique
Azad, S. Kazemzadeh; Hasançebi, Oğuzhan (2015-08-01)
The guided stochastic search (GSS) is a computationally efficient design optimization technique, which is originally developed for discrete sizing optimization problems of steel trusses with a single displacement constraint under a single load case. The present study aims to investigate the GSS in a more general class of truss sizing optimization problems subject to multiple displacement constraints and load cases. To this end, enhancements of the GSS are proposed in the form of two alternative approaches t...
An elitist self-adaptive step-size search for structural design optimization
Azad, S. Kazemzadeh; Hasançebi, Oğuzhan (2014-06-01)
This paper presents a method for optimal sizing of truss structures based on a refined self-adaptive step-size search (SASS) algorithm. An elitist self-adaptive step-size search (ESASS) algorithm is proposed wherein two approaches are considered for improving (i) convergence accuracy, and (ii) computational efficiency. In the first approach an additional randomness is incorporated into the sampling step of the technique to preserve exploration capability of the algorithm during the optimization. Furthermore...
Citation Formats
O. Hasançebi and M. p. Saka, “A reformulation of the ant colony optimization algorithm for large scale structural optimization,” Chania, Crete, Greece, 2011, vol. 97, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84894116379&origin=inward.