School-based optimization for performance-based optimum seismic design of steel frames

The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

     

Enhanced hybrid metaheuristic algorithms for optimal sizing of steel truss structures with numerous discrete variables

The advent of modern computing technologies paved the way for development of numerous efficient structural design optimization tools in the recent decades. In the present study sizing optimization problem of steel truss structures having numerous discrete variables is tackled using combined forms of recently proposed metaheuristic techniques. Three guided, and three guided hybrid metaheuristic algorithms are developed by integrating a design oriented strategy to the stochastic search properties of three recently proposed metaheuristic optimization techniques, namely adaptive dimensional search, modified big bang-big crunch, and exponential big bang-big crunch algorithms. The performances of the proposed guided, and guided hybrid metaheuristic algorithms are compared to those of standard variants through optimum design of real-size steel truss structures with up to 728 design variables according to AISC-LRFD specification. The numerical results reveal that the hybrid form of adaptive dimensional search and exponential big bang-big crunch algorithm is the most promising algorithm amongst the other investigated techniques.     

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 comparison of simulated annealing and genetic algorithm for optimum design of nonlinear steel space frames

In this article, two algorithms are presented for the optimum design of geometrically nonlinear steel space frames that are based on simulated annealing and genetic algorithm. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as the American Institute of Steel Construction (AISC) wide-flange shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) and AISC Allowable Stress Design (ASD) specifications, maximum (lateral displacement) and interstorey drift constraints, and also size constraints for columns were imposed on frames. The algorithms were applied to the optimum design of three space frame structures, which have a very small amount of nonlinearity. The unconstrained form of objective function was applied in both optimum design algorithms, and constant penalty factors were used instead of gradually increasing ones. Although genetic algorithm took much less time to converge, the comparisons showed that the simulated annealing algorithm yielded better designs together with AISC-LRFD code specification.     

Optimal seismic design of 3D steel moment frames: different ductility types

Three different types of lateral resisting steel moment frames consisting of ordinary moment frame (OMF), intermediate moment frame (IMF) and special moment frame (SMF) are available for design of 3D frames in literature. In this paper, optimum seismic design of 3D steel moment frames with different types of lateral resisting systems are performed according to the AISC-LRFD design criteria. A comparison is made considering the results of the above mentioned frames of different ductility types. These frames are analyzed by Response Spectrum Analysis (RSA), and optimizations are performed using nine different well-established metaheuristic algorithms. Performances of these algorithms are then compared for introducing the most suitable metaheuristic algorithms for optimal design of the 3D frames.     

Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures

In recent years a number of metaheuristic search techniques have been widely used in developing structural optimization algorithms. Amongst these techniques are genetic algorithms, simulated annealing, evolution strategies, particle swarm optimizer, tabu search, ant colony optimization and harmony search. The primary goal of this paper is to objectively evaluate the performance of abovementioned seven techniques in optimum design of pin jointed structures. First, a verification of the algorithms used to implement the techniques is carried out using a benchmark problem from the literature. Next, the techniques compiled in an unbiased coding platform are evaluated and compared in terms of their solution accuracies as well as convergence rates and reliabilities using four real size design examples formulated according to the design limitations imposed by ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution). The results reveal that simulated annealing and evolution strategies are the most powerful techniques, and harmony search and simple genetic algorithm methods can be characterized by slow convergence rates and unreliable search performance in large-scale problems.     

Water wave optimization: A new nature-inspired metaheuristic

Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena of water waves, such as propagation, refraction, and breaking, can be used to derive effective mechanisms for searching in a high-dimensional solution space. In general, the algorithmic framework of WWO is simple, and easy to implement with a small-size population and only a few control parameters. We have tested WWO on a diverse set of benchmark problems, and applied WWO to a real-world high-speed train scheduling problem in China. The computational results demonstrate that WWO is very competitive with state-of-the-art evolutionary algorithms including invasive weed optimization (IWO), biogeography-based optimization (BBO), bat algorithm (BA), etc. The new metaheuristic is expected to have wide applications in real-world engineering optimization problems.     

Efficient optimum seismic design of reinforced concrete frames with nonlinear structural analysis procedures

Performance-based seismic design offers enhanced control of structural damage for different levels of earthquake hazard. Nevertheless, the number of studies dealing with the optimum performance-based seismic design of reinforced concrete frames is rather limited. This observation can be attributed to the need for nonlinear structural analysis procedures to calculate seismic demands. Nonlinear analysis of reinforced concrete frames is accompanied by high computational costs and requires a priori knowledge of steel reinforcement. To address this issue, previous studies on optimum performance-based seismic design of reinforced concrete frames use independent design variables to represent steel reinforcement in the optimization problem. This approach drives to a great number of design variables, which magnifies exponentially the search space undermining the ability of the optimization algorithms to reach the optimum solutions. This study presents a computationally efficient procedure tailored to the optimum performance-based seismic design of reinforced concrete frames. The novel feature of the proposed approach is that it employs a deformation-based, iterative procedure for the design of steel reinforcement of reinforced concrete frames to meet their performance objectives given the cross-sectional dimensions of the structural members. In this manner, only the cross-sectional dimensions of structural members need to be addressed by the optimization algorithms as independent design variables. The developed solution strategy is applied to the optimum seismic design of reinforced concrete frames using pushover and nonlinear response-history analysis and it is found that it outperforms previous solution approaches.     

Optimum design of high-rise steel buildings using an evolution strategy integrated parallel algorithm

The present study addresses a parallel solution algorithm for optimum design of large steel space frame structures, in particular high-rise steel buildings. The algorithm implements a novel discrete evolution strategy optimization method to effectively size these systems for minimum weight according to the provisions of ASD-AISC specification and various practical aspects of design process. The multitasking environment in the algorithm rests on a master–slave model based parallelization of the optimization procedure, which provides an ideal platform for attaining optimal solutions in a timely manner without losing accuracy in computations. Three design examples from the category of high-rise steel buildings are studied extensively to demonstrate cost-efficiency of the algorithm in conjunction with a cluster of computers with 32 processors. The variation in performance of the parallel computing system with respect to the number of processors employed is also scrutinized in each design example.     

Optimum load and resistance factor design of steel space frames using genetic algorithm

In this paper, an algorithm is presented for the minimum weight design of steel moment-resisting space frames subjected to American Institute of Steel Construction (AISC) Load and Resistance Factor Design (LRFD) specification. A genetic algorithm (GA) is utilized herein as the optimization method. Design variables which are cross-sectional areas are discrete and are selected from the standard set of AISC wide-flange (W) shapes. The structure is subjected to wind loading in accordance with the Uniform Building Code (UBC) in conjunction with vertical loads (dead and live loads). Displacement and AISC LRFD stress constraints are imposed on the structure. The algorithm is applied to the design of three space frame structures. The designs obtained using AISC LRFD code are compared to those where AISC Allowable Stress Design (ASD) is considered. The comparisons show that the former code results in lighter structures for the examples presented. Received November 15, 1999     

Optimum weight analysis of steel frames with semirigid connections

A fact that should be initially emphasized is that this research work does not mainly aim at development of optimization techniques which have already been well established for some time ago. Our aim here is only to make use of these techniques for another purpose namely; to obtain the optimum weight solution for steel frames using the semirigid connections concept. The purpose of this study was to determine the following: (1) The percent of rigidity of the semirigid connections that would give the optimum steel weight for the common types of steel frame structures. (2) The exact percent of steel saving when using the semirigid connections concept, compared to the classical approach. (3) Which is cheaper, the gable or the portal frame, considering the same condition of loading and geometrical configuration. Also the relative cheapness of the double bay or triple-bay multistory frames having the same number of storys and also same conditions of loading and height of columns and total span. (4) Whether the deflection is a governing parameter which might hinder the benefits of the use of semirigid connections. The problem here is to find the optimum weight of plane semirigid connected steel frames. The objective function is given by the weight of structure in terms of the geometrical properties of the elements and the density of steel. The design variables are the breadth of flange and the height of web for each element. For the portal, the gable, the triple bay three story, and the double bay three story frames the number of design variables are four, four, ten, and ten respectively. The design constraints represent strength and stiffness design code requirements. Side constraints are respected to assure nonviolating of the practical available dimensions. The model is solved using the nonlinear programming techniques. The unconstrained formulation using the interior penalty function technique is selected. Powell's algorithm is chosen for the generation of the search vector, and the quadratic interpolation technique is chosen for the determination of the step size. The conclusions are as follows: (1) The use of semirigid connections will provide a saving in weight equal to 28% in the portal frame for corresponding rigidity ratio (R.R.) of 0.9, and a saving of 19% in the gable frame for a corresponding R.R. of 0.733. (2) The use of semirigid connections will provide a saving in weight of 10.75% in the double bay three story frame for a corresponding R.R = 0.733, and 23% for the triple bay three story frame with a corresponding R.R = 0.75. (3) The variation in the percent of the rigidity of the column to girder connection will not cause a corresponding change in the amount of steel allocated to columns and girders. (4) The saving in weight will occur mainly due to girders rather than columns. (5) The vertical deflections were not regarded as a dominating factor as its value did not exceed the allowable limits.     

Harmony search algorithm for minimum cost design of steel frames with semi-rigid connections and column bases

Harmony search-based algorithm is developed to determine the minimum cost design of steel frames with semi-rigid connections and column bases under displacement, strength and size constraints. Harmony search (HS) is recently developed metaheuristic search algorithm which is based on the analogy between the performance process of natural music and searching for solutions of optimum design problems. The geometric non-linearity of the frame members, the semi-rigid behaviour of the beam-to-column connections and column bases are taken into account in the design algorithm. The results obtained by semi-rigid connection and column base modelling are also compared to one developed by rigid connection modelling. The efficiency of HS algorithm, in comparison with genetic algorithms (GAs), is verified with three benchmark examples. The results indicate that HS could obtain lighter frames and less cost values than those developed using GAs.     

The effects of rehabilitation objectives on near optimal trade-off relation between minimum weight and maximum drift of 2D steel X-braced frames considering soil-structure interaction using a cluster-based NSGA II

A cluster-based non-dominated sorting genetic algorithm (NSGA) II has been considered to investigate the effects of rehabilitation objectives on multi-objective design optimization of two-dimensional (2D) steel X-braced frames in the presence of soil-structure interaction. The substructure elasto-perfect plastic model has been adopted for modeling of the soil-structure interaction and the nonlinear pushover analysis is used to evaluate the performance level of the frames for a specified hazard level. Cross-sections of grouped elements of the frames are considered to be discontinuous design variables of the problem. Via implementing some of the constraints, which are independent of doing the time-consuming nonlinear analysis, input population of the optimization technique has been clustered. By using the nonlinear analysis technique in conjunction with the cluster-based NSGA II, near optimal trade-off relation between minimum weight and maximum story drifts of the frames are obtained. The allowable rotations, geometry, and resistance constraints of the structural elements are considered in the optimization design of the frames. The effects of the enhanced basic safety and limited selective rehabilitation objectives on optimum design of the frame are studied. The results show differences between the optimum results of the three mentioned rehabilitation objectives and effects of soil types.

     

Cohort intelligence algorithm for discrete and mixed variable engineering problems

Abstract

In this study, the performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems. The investigated problems are mainly adopted from discrete structural optimization and mixed variable mechanical engineering design domains. For handling the discrete solution variables, a round off integer sampling approach is proposed. Furthermore, in order to deal with the nonlinear constraints, a penalty function method is incorporated. The obtained results are promising and computationally more efficient when compared to the other existing optimization techniques including a Multi Random Start Local Search algorithm. The associated advantages and disadvantages of CI algorithm are also discussed evaluating the effect of its two parameters namely the number of candidates, and sampling space reduction factor.     

基于变异和信息素扩散的多维背包问题的蚁群算法

针对蚁群算法在求解大规模多维背包问题时存在的迭代次数过多、精度不高的不足,提出一种新的高性能的蚁群求解算法.算法将信息素更新和随机搜索机制的改进相融合.首先,基于对较优解的偏爱,采用Top-k策略从每次迭代的k个解中挖掘出对象间的关联距离;其次,以对象为信源借助关联距离建立信息素的扩散模型,通过信息素扩散的耦合补偿,强化了蚂蚁间的协作和交流;最后,利用一种简单的变异策略对迭代的结果进行优化.在通用数据集上的大量实验表明:与最新的蚁群算法相比,新算法不仅能获得更好的最优解,而且收敛速度有显著的提高.     

Smart dispatching and metaheuristic swarm flow algorithm

The resource saving dispatching aims at finding the optimal combination of powers produced by power generating units that minimizes the total costs subject to given constraints. A metaheuristic swarm flow algorithm is proposed. Results of the comparative analysis of the efficiency of this algorithm on benchmark problems are presented. The comparison was performed with the particle swarm optimization, genetic, and biogeography-based optimization algorithms using systems consisting of 6 and 20 power generating units as examples. The flow algorithm converges to the optimal solution using less computational resources.     

Optimum design of steel frames using harmony search algorithm

In this article, harmony search algorithm was developed for optimum design of steel frames. Harmony search is a meta-heuristic search method that has been developed recently. It bases on the analogy between the performance process of natural music and searching for solutions to optimization problems. The objective of the design algorithm is to obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Strength constraints of AISC load and resistance factor design specification and displacement constraints were imposed on frames. The effectiveness and robustness of harmony search algorithm, in comparison with genetic algorithm and ant colony optimization-based methods, were verified using three steel frames. The comparisons showed that the harmony search algorithm yielded lighter designs.     

A survey of symbiotic organisms search algorithms and applications

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.

     

Optimum weight design of steel space frames with semi-rigid connections using harmony search and genetic algorithms

In this paper, an optimization process using MATLAB-SAP2000 Open Application Programming Interface (OAPI) is presented for optimum design of space frames with semi-rigid connections. A specified list including W-profiles taken from American Institute of Steel Construction (AISC) is used in the selection of suitable sections. The stress constraints as indicated in load and resistance factor design of AISC, lateral displacement constraints being the top- and inter-storey drift and geometric constraints are considered in the optimization process. Genetic algorithm method based on biological principles and harmony search algorithm method based on the processes of musical harmony are used for optimum designs. Two different space frames are solved for the cases of rigid and semi-rigid connections, separately. A computer program is coded in MATLAB for the purpose interacting with SAP2000 OAPI. Results obtained from the analyses show that type of semi-rigid connections plays a crucial role in the optimization of steel space frames and increases the optimum weight.

     

An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean–variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology.