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Optimal design of large-scale structures is a rather difficult task and the computational efficiency of the currently available methods needs to be improved. In view of this, the paper presents a modified Charged System Search (CSS) algorithm. The new methodology is based on the combination of CSS and Particle Swarm Optimizer. In addition, in order to improve optimization search, the sequence of tasks entailed by the optimization process is changed so that the updating of the design variables can directly be performed after each movement. In this way, the new method acts as a single-agent algorithm while preserving the positive characteristics of its original multi-agent formulation.

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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 Design Code of American Institute of Steel Institution) specification. The results obtained are used to discuss the computational characteristics of ACO for optimum design of truss type structures.

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Many methods have been developed for structural size and configuration optimization in which cross-sectional areas are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes two efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat both discrete sizing variables and integrated discrete sizing and continuous geometric variables. The HS algorithm uses a stochastic random search instead of a gradient search so the former has a new-paradigmed derivative. Several truss examples from the literature are also presented to demonstrate the effectiveness and robustness of the new method, as compared to current optimization methods.

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Earthquake time history records are required to perform dynamic nonlinear analyses. In order to provide a suitable set of such records, they are scaled to match a target spectrum as introduced in the well-known design codes. Corresponding scaling factors are taken similar in practice however, optimizing them reduces extra-ordinary economic charge for the seismic design. In the present work a new hybrid meta-heuristic is developed combining key features from genotypic search and particle swarm optimization. The method is applied to an illustrative example via a parametric study to evaluate its effectiveness and less probability of premature convergence compared with the standard particle swarm optimization.

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In some cases the optimum is the minimum of the objective function (mathematical optimum), but in other cases the optimum is given by a technical constraint (technical optimum). The present paper shows the both types in two problems. The first problem is to find the optimum dimensions of a ring-stiffened circular cylindrical shell subject to external pressure, which minimize the structural cost. The calculation shows that the cost decreases when the shell diameter decreases. The decrease of diameter is limited by a fabrication constraint that the diameter should be minimum 2 m to make it possible the welding and painting inside of the shell. The second problem is to find the optimum dimensions of a cantilever column loaded by compression and bending. The column is constructed as circular or conical unstiffened shell. The cost comparison of both structural versions shows the most economic one.

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Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimization problems, is employed for size optimization of steel space trusses. Design examples reveal competitive performance of the algorithm compared to the other advanced metaheuristics.

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Nature-inspired search algorithms have proved to be successful in solving real-world optimization problems. Firefly algorithm is a novel meta-heuristic algorithm which simulates the natural behavior of fireflies. In the present study, optimum design of truss structures with both sizing and geometry design variables is carried out using the firefly algorithm. Additionally, to improve the efficiency of the algorithm, modifications in the movement stage of artificial fireflies are proposed. In order to evaluate the performance of the proposed algorithm, optimum designs found are compared to the previously reported designs in the literature. Numerical results indicate the efficiency and robustness of the proposed approach.

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Heavy economic losses and human casualties caused by destructive earthquakes around the world clearly show the need for a systematic approach for large scale damage detection of various types of existing structures. That could provide the proper means for the decision makers for any rehabilitation plans. The aim of this study is to present an innovative method for investigating the seismic vulnerability of the existing concrete structures with moment resisting frames (MRF). For this purpose, a number of 2-D structural models with varying number of bays and stories are designed based on the previous Iranian seismic design code, Standard 2800 (First Edition). The seismically–induced damages to these structural models are determined by performing extensive nonlinear dynamic analyses under a number of earthquake records. Using the IDARC program for dynamic analyses, the Park and Ang damage index is considered for damage evaluation of the structural models. A database is generated using the level of induced damages versus different parameters such as PGA, the ratio of number of stories to number of bays, the dynamic properties of the structures models such as natural frequencies and earthquakes. Finally, in order to estimate the vulnerability of any typical reinforced MRF concrete structures, a number of artificial neural networks are trained for estimation of the probable seismic damage index.

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The main aim of the present study is to propose a modified harmony search (MHS) algorithm for size and shape optimization of structures. The standard harmony search (HS) algorithm is conceptualized using the musical process of searching for a perfect state of the harmony. It uses a stochastic random search instead of a gradient search. The proposed MHS algorithm is designed based on elitism. In fact the MHS is a multi-staged version of the HS and in each stage a new harmony memory is created using the information of the previous stages. Numerical results reveal that the proposed algorithm is a powerful optimization technique with improved exploitation characteristics compared with the standard HS.

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In this paper nonlinear analysis of structures are performed considering material and geometric nonlinearity using force method and energy concepts. For this purpose, the complementary energy of the structure is minimized using ant colony algorithms. Considering the energy term next to the weight of the structure, optimal design of structures is performed. The first part of this paper contains the formulation of the complementary energy of truss and frame structures for the purpose of linear analysis. In the second part material and geometric nonlinearity of structure is considered using Ramberg-Osgood relationships. In the last part optimal simultaneous analysis and design of structure is studied. In each part, the efficiency of the methods is illustrated by means simple examples.

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Different kinds of meta-heuristic algorithms have been recently utilized to overcome the complex nature of optimum design of structures. In this paper, an integrated optimization procedure with the objective of minimizing the self-weight of real size structures is simply performed interfacing SAP2000 and MATLAB® softwares in the form of parallel computing. The meta-heuristic algorithm chosen here is Cuckoo Search (CS) recently developed as a type of population based algorithm inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior. The CS algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations and displacement constraints are imposed on frames. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool are also some new features of the present study. The results show that the proposed method is effective in optimizing practical structures.

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The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed algorithm is investigated in typical sizing and layout optimization problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.

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This paper investigates the optimized parameters for the tuned liquid column dampers to decrease the earthquake vibrations of high-rise buildings. Considering soil effects, the soilstructure interaction (SSI) is involved in this model. The Tuned Liquid Column Damper (TLCD) is also utilized on the roof of the building. Since the TLCD is a nonlinear device, the time domain analysis based on nonlinear Newmark method is employed to obtain the displacement, velocity and acceleration of different stories and TLCD. To illustrate the results, Kobe earthquake data is applied to the model. In order to obtain the best settings for TLCD, different parameters of TLCD are examined with constant mass quantity. The effective length, head loss coefficient, cross sectional ratio and length ratio of TLCD are assumed as the design variables. The objective is to reduce the maximum absolute and Root Mean Square (RMS) values of displacement and acceleration during earthquake vibration. The results show that the TLCDs are very effective and beneficial devices for decreasing the oscillations of high-rise buildings. It is indicated that the soil type highly affects the suitable parameters of TLCD subjected to the earthquake oscillations. This study helps the researchers to the better understanding of earthquake vibration of the structures including soil effects, and leads the designers to achieve the optimized TLCD for the high-rise buildings.

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This paper presents the study of the optimized bi-dimensional wood structures, truss type, applying the method of genetic algorithms. Assessment is performed by means of a computer program called OPS (Optimization of Plane Structures). The purpose is to meet the optimum geometric configuration taking into account the volume reduction. Different strategies are considered for the positioning of diagonals and struts in the upper chord. It is concluded that the trussed system efficiency depends on the dimensions and the position of the members, where the purlin’s location is not mandatory for struts and diagonal positions.

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In this article optimal design of shear walls is performed under seismic loading. For practical aims, a database of special shear walls is created. Special shear walls are used for seismic design optimization employing the charged system search algorithm as an optimizer. Constraints consist of design and performance limitations. Nonlinear behavior of the shear wall is taken into account and performance based seismic design optimization is accomplished. Capacity curves of the optimal solution are determined and compared incorporates soil–structure interaction. Also an optimization based method is proposed for bilinear approximation of capacity curve. These are a new methodology for seismic RC shear wall optimum design.

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In the present study, the computational performance of the particle swarm optimization (PSO) harmony search (HS) and firefly algorithm (FA), as popular metaheuristics, is investigated for size and shape optimization of truss structures. The PSO was inspired by the social behavior of organisms such as bird flocking. The HS imitates the musical performance process which takes place when a musician searches for a better state of harmony, while the FA was based on the idealized behavior of the flashing characteristics of natural fireflies. These algorithms were inspired from different natural sources and their convergence behavior is focused in this paper. Several benchmark size and shape optimization problems of truss structures are solved using PSO, HS and FA and the results are compared. The numerical results demonstrate the superiority of FA to HS and PSO.

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Based on introducing two optimization algorithms, group search optimization (GSO) algorithm and particle swarm optimization (PSO) algorithm, a new hybrid optimization algorithm which named particle swarm-group search optimization (PS-GSO) algorithm is presented and its application to optimal structural design is analyzed. The PS-GSO is used to investigate the spatial truss structures with discrete variables and is tested by truss optimization problems. The optimization results are compared with that of the HPSO and GSO algorithm. The results show that the PS-GSO is able to accelerate the convergence rate effectively and has the fastest convergence rate among these three algorithms. The research shows the proposed PS-GSO algorithm can be effectively applied to optimal design of spatial structures with discrete variables.

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A novel two-stage algorithm for detection of damages in large-scale structures under static loads is presented. The technique utilizes the vector of response change (VRC) and sensitivities of responses with respect to the elemental damage parameters (RSEs). It is shown that VRC approximately lies in the subspace spanned by RSEs corresponding to the damaged elements. The property is leveraged in the first stage of the proposed method by seeking RSEs whose spanned subspace best contains the VRC. Consequently, the corresponding elements are regarded as damage candidates. To alleviate the exploration among RSEs, they are first partitioned into several clusters. Subsequently, discrete ant colony optimization (ACO) is utilized to find the clusters containing the RSEs of damaged elements. In the second stage of the algorithm, damage amounts for the restricted elements are determined using a continuous version of ACO. Two numerical examples are studied. The results illustrate that the method is both robust and efficient for detection of damages in large-scale structures.

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Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers. These algorithms have been used in the solution of various structural optimization problems where the main goal is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, artificial bee colony algorithm (ABC) is utilized to optimize different skeletal structures. The results of the ABC are compared with the results of other optimization algorithms from the literature to show the efficiency of this technique for structural design problems.

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Water cycle algorithm (WCA) is a new metaheuristic algorithm which the fundamental concepts of WCA are derived from nature and are based on the observation of water cycle process and how rivers and streams flow to sea in the real world. In this paper, the task of sizing optimization of truss structures including discrete and continues variables carried out using WCA, and the optimization results were compared with other well-known optimizers. The obtained statistical results show that the WCA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers.