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Showing 31 results for Truss Structure

Ali Kaveh, Siamak Talatahari,
Volume 1, Issue 1 (3-2011)
Abstract

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.
O. Hasançebi, S. Çarbaş,
Volume 1, Issue 1 (3-2011)
Abstract

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.
K.s. Lee, S.w. Han, Z.w. Geem,
Volume 1, Issue 1 (3-2011)
Abstract

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.
A. Kaveh, M. Kalateh-Ahani, M.s. Masoudi,
Volume 1, Issue 2 (6-2011)
Abstract

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.
S. Kazemzadeh Azad, S. Kazemzadeh Azad ,
Volume 1, Issue 2 (6-2011)
Abstract

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.
A. Hadidi, A. Kaveh, B. Farahmand Azar, S. Talatahari, C. Farahmandpour,
Volume 1, Issue 3 (9-2011)
Abstract

In this paper, an efficient optimization algorithm is proposed based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to optimize truss structures. The proposed algorithm utilizes the PSO for finding high fitness regions in the search space and the SA is used to perform further investigation in these regions. This strategy helps to use of information obtained by swarm in an optimal manner and to direct the agents toward the best regions, resulting in possible reduction of the number of particles. To show the computational advantages of the new PSO-SA method, some benchmark numerical examples are studied. The PSO-SA algorithm converges to better or at least the same solutions, while the number of structural analyses is significantly reduced
S. Gholizadeh, A. Barzegar , Ch. Gheyratmand,
Volume 1, Issue 3 (9-2011)
Abstract

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.
S. Kazemzadeh Azad , S. Kazemzadeh Azad, A. Jayant Kulkarni,
Volume 2, Issue 1 (3-2012)
Abstract

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.
S. Gholizadeh, H. Barati,
Volume 2, Issue 3 (7-2012)
Abstract

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.
S.k. Zeng, L.j. Li,
Volume 2, Issue 4 (10-2012)
Abstract

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.
S. Shojaee, M. Arjomand, M. Khatibinia,
Volume 3, Issue 1 (3-2013)
Abstract

An efficient method for size and layout optimization of the truss structures is presented in this paper. In order to this, an efficient method by combining an improved discrete particle swarm optimization (IDPSO) and method of moving asymptotes (MMA) is proposed. In the hybrid of IDPSO and MMA, the nodal coordinates defining the layout of the structure are optimized with MMA, and afterwards the results of MMA are used in IDPSO to optimize the cross-section areas. The results show that the hybrid of IDPSO and MMA can effectively accelerate the convergence rate and can quickly reach the optimum design.
H. Eskandar, A. Sadollah , A. Bahreininejad,
Volume 3, Issue 1 (3-2013)
Abstract

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.
M. Shahrouzi , A. Yousefi,
Volume 3, Issue 1 (3-2013)
Abstract

Meta-heuristics have already received considerable attention in various engineering optimization fields. As one of the most rewarding tasks, eigenvalue optimization of truss structures is concerned in this study. In the proposed problem formulation the fundamental eigenvalue is to be maximized for a constant structural weight. The optimum is searched using Particle Swarm Optimization, PSO and its variant PSOPC with Passive Congregation as a recent meta-heuristic. In order to make further improvement an additional hybrid PSO with genetic algorithm is also proposed as PSOGA with the idea of taking benefit of various movement types in the search space. A number of benchmark examples are then treated by the algorithms. Consequently, PSOGA stood superior to the others in effectiveness giving the best results while PSOPC had more efficiency and the least fit ones belonged to the Standard PSO.
A. Kaveh, V.r Kalatjari, M.h Talebpour , J. Torkamanzadeh,
Volume 3, Issue 1 (3-2013)
Abstract

Different methods are available for simultaneous optimization of cross-section, topology and geometry of truss structures. Since the search space for this problem is very large, the probability of falling in local optimum is considerably high. On the other hand, different types of design variables (continuous and discrete) lead to some difficulties in the process of optimization. In this article, simultaneous optimization of cross-section, topology and geometry of truss structures is performed by utilizing the Multi Heuristic based Search Method (MHSM) that overcome the above mentioned problem and obtains good results. The presented method performs the optimization by dividing the searching space into five subsections in which an MHSM is employed. These subsections are named procedure islands. Some examples are then presented to scrutinize the method more carefully. Results show the capabilities of the present algorithm for optimal design of truss structures.
A. Ahrari, A. A. Atai,
Volume 3, Issue 2 (6-2013)
Abstract

The prevalent strategy in the topology optimization phase is to select a subset of members existing in an excessively connected truss, called Ground Structure, such that the overall weight or cost is minimized. Although finding a good topology significantly reduces the overall cost, excessive growth of the size of topology space combined with existence of varied types of design variables challenges applicability of evolutionary algorithms tailored for simultaneous optimization of topology, shape and size (TSS) in more complicated cases which are of great practical interest. In practice, large-scale truss structures are often modular, formed by joining periodically repeated units. This article organizes a novel simulation approach for this class of truss structures where the main drawbacks of the ground structure-based simulation approach are greatly moderated. The two approaches are independently employed for simultaneous TSS optimization of a modular truss example and the size of topology space as well as the required computation budget to generate an acceptable candidate design is compared. Result comparison reveals by employing the novel approach, problem complexity grows linearly with respect to the number of modules which allows for expanding application of TSS optimizers to complex modular trusses. Use of relative coordinates is also warranted for shape optimization which concludes to a more efficient optimization process.
O. Hasançebi, S. Kazemzadeh Azad, S. Kazemzadeh Azad,
Volume 3, Issue 2 (6-2013)
Abstract

The present study attempts to apply an efficient yet simple optimization (SOPT) algorithm to optimum design of truss structures under stress and displacement constraints. The computational efficiency of the technique is improved through avoiding unnecessary analyses during the course of optimization using the so-called upper bound strategy (UBS). The efficiency of the UBS integrated SOPT algorithm is evaluated through benchmark sizing optimization problems of truss structures and the numerical results are reported. A comparison of the numerical results attained using the SOPT algorithm with those of modern metaheuristic techniques demonstrates that the employed algorithm is capable of locating promising designs with considerably less computational effort.
W. Cheng, F. Liu , L.j. Li,
Volume 3, Issue 3 (9-2013)
Abstract

A novel optimization algorithm named teaching-learning-based optimization (TLBO) algorithm and its implementation procedure were presented in this paper. TLBO is a meta-heuristic method, which simulates the phenomenon in classes. TLBO has two phases: teacher phase and learner phase. Students learn from teachers in teacher phases and obtain knowledge by mutual learning in learner phase. The suitability of TLBO for size and geometry optimization of structures in structural optimal design was tested by three truss examples. Meanwhile, these examples were used as benchmark structures to explore the effectiveness and robustness of TLBO. The results were compared with those of other algorithms. It is found that TLBO has advantages over other optimal algorithms in convergence rate and accuracy when the number of variables is the same. It is much desired for TLBO to be applied to the tasks of optimal design of engineering structures.
L. J. Li, Z. H. Huang,
Volume 4, Issue 2 (6-2014)
Abstract

This paper presents an improved multi-objective group search optimizer (IMGSO) that is based on Pareto theory that is designed to handle multi-objective optimization problems. The optimizer includes improvements in three areas: the transition-feasible region is used to address constraints, the Dealer’s Principle is used to construct the non-dominated set, and the producer is updated using a tabu search and a crowded distance operator. Two objective optimization problems, the minimum weight and maximum fundamental frequency, of four truss structures were optimized using the IMGSO. The results show that IMGSO rapidly generates the non-dominated set and is able to handle constraints. The Pareto front of the solutions from IMGSO is clearly dominant and has good diversity.
A. Kaveh , P. Hosseini,
Volume 4, Issue 3 (9-2014)
Abstract

Simplified Dolphin Echolocation (SDE) optimization is an improved version of the Dolphin Echolocation optimization. The dolphin echolocation (DE) is a recently proposed metaheuristic algorithm, which was imitated dolphin’s hunting process. The global or near global optimum solution modeled as dolphin’s bait, dolphins send sound in different directions to discover the best bait among their search space. This paper introduced a new optimization method called SDE for weight optimization of steel truss structures problems. SDE applies some new approaches for generating new solutions. These improvements enhance the accuracy and convergence rate of the DE SDE does not depend on any empirical parameter. The results of the SDE for mathematical and engineering optimization problems are compared to those of the standard DE and some popular metaheuristic algorithms. The results show that SDE is competitive with other algorithms.
S. Gholizadeh , H. Asadi , A. Baghchevan,
Volume 4, Issue 3 (9-2014)
Abstract

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvements are implemented on the basic algorithms. The proposed MOOAs are examined for three truss optimization problems, and the results are compared to those of some other well-known methods. The numerical results demonstrate that the proposed MOOAs possess better computational performance compared to the other algorithms.

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