Bat inspired algorithm for discrete size optimization of steel frames

Bat inspired (BI) algorithm is a recently developed metaheuristic optimization technique inspired by echolocation behavior of bats. In this study, the BI algorithm is examined in the context of discrete size optimization of steel frames designed for minimum weight. In the optimum design problem frame members are selected from available set of steel sections for producing practically acceptable designs subject to strength and displacement provisions of American Institute of Steel Construction-Allowable Stress Design (AISC-ASD) specification. The performance of the technique is quantified using three real-size large steel frames under actual load and design considerations. The results obtained provide a sufficient evidence for successful performance of the BI algorithm in comparison to other metaheuristics employed in structural optimization

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)

Discrete size optimization of steel trusses using a refined big bang-big crunch algorithm

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang-big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures

Improving the big bang-big crunch algorithm for optimum design of steel frames

This paper presents an improved version of the big bang-big crunch (BB-BC) algorithm namely exponential BB-BC algorithm (EBB-BC) for optimum design of steel frames according to ASD-AISC provisions. It is shown that the standard version of the algorithm sometimes is unable to provide reasonable solutions for problems from discrete design optimization of steel frames. Therefore, by investigating the shortcomings of the BB-BC algorithm, it is aimed to enhance the algorithm for solving complicated steel frame optimization problems. In order to evaluate the performance of the proposed algorithm, the optimization results attained using the EBB-BC algorithm are compared to those of other well known metaheuristics. The numerical results demonstrate the efficiency and robustness of the proposed algorithm in practical design optimization of steel frames

Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique

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 that enable the technique to deal with multiple displacement/load cases. The first approach implements a methodology in which the most critical displacement direction is considered only when guiding the search process. The second approach, however, takes into account the cumulative effect of all the critical displacement directions in the course of optimization. Advantage of the integrated force method of structural analysis is also utilized for further reduction of the computational effort in these approaches. The proposed enhancements of GSS are investigated and compared with some selected techniques of design optimization through six truss structures that are sized for minimum weight. The numerical results reveal that both enhancements generally provide promising solutions with an insignificant computational effort

Computationally efficient discrete sizing of steel frames via guided stochastic search heuristic

Recently a design-driven heuristic approach named guided stochastic search (GSS) technique has been developed by the authors as a computationally efficient method for discrete sizing optimization of steel trusses. In this study, an extension and reformulation of the GSS technique are proposed for its application to problems from discrete sizing optimization of steel frames. In the GSS, the well-known principle of virtual work as well as the information attained in the structural analysis and design stages are used together to guide the optimization process. A design wise strategy is employed in the technique where resizing of members is performed with respect to their role in satisfying strength and displacement constraints. The performance of the GSS is investigated through optimum design of four steel frame structures according to AISC-LRFD specifications. The numerical results obtained demonstrate that the GSS can be employed as a computationally efficient design optimization tool for practical sizing optimization of steel frames

An exponential big bang-big crunch algorithm for discrete design optimization of steel frames

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 this class of problems in particular. The study provides numerical evidences of how the performance of a well-known metaheuristic technique (BB-BC) can significantly be improved through simple yet effective modifications in its formulation to achieve a more robust and reliable variant (EBB-BC). Further, a comparison of the optimum solutions with those of other well-known metaheuristics reveals that the EBB-BC algorithm can efficiently be used as a novel tool for code based design optimization of steel frames

An elitist self-adaptive step-size search for structural design optimization

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, an adaptive sampling scheme is introduced to enhance quality of the final solutions. In the second approach computational efficiency of the technique is accelerated through avoiding unnecessary analyses throughout the optimization process using the so-called upper bound strategy (UBS). The numerical results indicate the efficiency of the proposed ESASS algorithm

Improving Computational Efficiency of Bat-Inspired Algorithm in Optimal Structural Design

Bat-inspired (BI) algorithm is a recent metaheuristic optimization technique that simulates echolocation behavior of bats in seeking a design space. Along the same line with almost all metaheuristics, this algorithm also entails a large number of time-consuming structural analyses in structural design optimization applications. This study is focused on improving computational efficiency of the BI algorithm in optimum structural design. The number of structural analyses required by BI algorithm in the course of design optimization is reduced considerably by incorporating an upper bound strategy (UBS) into the solution procedure. The performance of the resulting algorithm, i.e. UBS integrated BI algorithm (UBI), is evaluated in discrete sizing optimization of large-scale steel skeletal structures designed for minimum weight according to American Institute of Steel Construction-Allowable Stress Design provisions. The numerical results verify that the UBI results in a significant gain in the computational efficiency of the standard algorithm