Optimization Of Truss Structures Research Articles

Colliding Bodies Optimization method for optimum discrete design of truss structures

This paper implements the recently developed meta-heuristic algorithm Colliding Bodies Optimization (CBO) for the optimization of truss structures with discrete sizing variables. The CBO algorithm is inspired by the laws of one-dimensional collision. After collision of two moving bodies, with specified mass and velocity, these bodies are separated and move to new positions with new velocities. Colliding bodies correspond to candidate designs. CBO utilizes a simple formulation and does not require parameter tuning. Test problems demonstrate the efficiency of the proposed approach.

Weight optimization of truss structures by a new feasible boundary search technique hybridized with firefly algorithm

In most metaheuristic optimization techniques, constraint handling is accomplished by traditional penalty function method which may generate infeasible solutions. The main objective of this paper is to introduce a new technique for constraint handling in size optimization of truss structures. The method restricts the searching space on the feasible boundary and hence it is referred to as Feasible Boundary Search (FBS) technique. By limiting the searching space on the feasible boundary, the problem constraints are automatically satisfied. The method generates absolutely feasible high-quality solutions. Its generality enables the scheme to be hybridized with most metaheuristics. By presenting some modifications on the standard Firefly Algorithm (FA), a powerful hybridized technique which is denoted by FBSFA is then developed for fast weight optimization of truss structures. Several design examples show the effectiveness, robustness and accuracy of FBSFA.

Finite Element Software for Optimization of Triangular Light Roof Truss Structure of Gymnasium

As the landmark building of whole city,the gymnasium represents the image of the city.The gymnasium must be clean,concise and grand in extrinsic feature.The gymnasium must also be stable,safety and deal with accidents.Building gymnasium no only is according to the appearance and safety,but also considers efficiency and cost in this era in which technology is developing very rapdily.Excess resources and large volume will lead to wasting material and time.For saving labor and resources, ANSYS is applied to optimize the size of I beams which are used to build the triangular light roof of gymnasium in this paper. According to the optimization results,the weight of structure is optimal when size of I beams are 150×150,stress of structure is in the safety margin at the same time.

The Great Salmon Run Metaheuristic for Robust Shape and Size Design of Truss Structures with Dynamic Constraints

In this investigation, the authors intend to demonstrate the applicability of a recent spotlighted metaheuristic called the great salmon run (TGSR) algorithm for shape and size design of truss structures. The algorithmic functioning of TGSR emulates the annual migration of salmons together with dangers laid through their pathways. In a previous study by the authors, it has been proved that the method is as effective as most of the state-of-the-art metaheuristics for a wide range of numerical benchmark problems. Here, the authors utilize TGSR together with some rival metaheuristics, i.e. bee algorithm (BA), scale factor local search differential evolutionary algorithm (SFLSDEA), chaotic particle swarm optimization (CPSO) algorithm, self adaptive penalty function genetic algorithm (SAPFGA) and mutable smart bee algorithm (MSBA), for optimal design of truss structures with dynamic frequency constraints. To effectively handle the constraints, the authors take the advantage of self-adaptive penalty function (SAPF) constraint handling technique to free the user from any priori penalty coefficient tuning. Therefore, an algorithm for automation of constraint shape and size design of truss structures is proposed here. Furthermore, for more elaboration, the authors consider the results of some previous reports for same problems to find out whether TGSR is capable of yielding comparative results as compared to other metaheuristics. Through the experiments, the exploration/exploitation capabilities of TGSR for truss design are investigated. It is proved that TGSR is not only able to handle the nonlinearities and decision making difficulties associated with shape and size optimization of truss structures but also can show comparative results as compared to powerful state-of-the-art metaheuristics.

Sizing Optimization Of Truss Structures Under Frequency Constraints With Artificial Bee Colony Algorithm

Due the wide applications of truss structures in industries including aviation, transportation, buildings and vital structures, design and optimization of these structures have became the most active research fields. Furthermore it turns out that in many of structures that have been exposed to wind, hurricanes or violent earthquake, the optimization according weight only is not an appropriate way. So recently, many efforts have been made according natural frequencies optimization and minimization of weight. Due to the importance of optimization and control of natural frequencies in structures and to avoid resonance, calculating of natural frequencies of the truss structures in different moods based on minimization of weight have been focused. To reach this goal method have been used in this research, optimization by Artificial Bee Colony algorithm (ABC). Optimized structures are a planar 10-bar truss and a space 72-bar truss. Conclusions show these method have better quality than other algorithms and they can use engineering complex structures optimization.

An efficient truss structure optimization framework based on CAD/CAE integration and sequential radial basis function metamodel

In order to improve the performance and efficiency of truss structure optimization, this paper presents a general framework that embeds and seamlessly integrates commercial CAD and CAE software through common programming languages and application programming interface (API). Along with the automatic CAD/CAE integration, an adaptive metamodel-based optimization called sequential radial basis function (SRBF) is applied to truss structure optimization involving sizing, geometry and topology variables. SRBF distinguishingly features two-loops searching strategy, the "inner loop" and the "outer loop". The "inner loop" aims to search a feasible point through updating the factors of the augmented Lagrangian function. With the improved significant sampling space (ISSS) method, the "outer loop" sequentially generates new additional samples to update the RBF model. The continuous relaxation method is developed to deal with the mixed-discrete variables during the truss structure optimization. Applied to practical truss structure optimization problems from small scale to large scale, the proposed framework demonstrates feasibility of the CAD/CAE integration system during the structure modeling and analysis, and facilitates the truss structure optimization process. The comparison results between the SRBF and other approaches show that SRBF improves merit of searching global optimum and reduces the computation cost.

Discrete Optimum Design for Truss Structures by Subset Simulation Algorithm

AbstractThis article deals with the design optimization of truss structures with discrete design variables, which remains quite a challenging task in structural design. A new discrete search strategy based on the recently developed subset simulation optimization algorithm is proposed in detail for this type of structural optimization. The discrete design variables are transformed into standard normal variable space to implement the sampling procedure in subset simulation optimization, while the optimization is processed in the discrete design space in the mean time. The performance of the proposed method is illustrated by four representative benchmark optimization problems. Comparisons are made with other well known stochastic optimization algorithms. It is found that the proposed method can produce optimum designs as good as or better than those of other stochastic optimization algorithms.

Colliding Bodies Optimization method for optimum design of truss structures with continuous variables

In recent years, the importance of economical considerations in the field of structures has motivated many researchers to propose new methods for minimizing the weight of the structures. In this paper, a new and simple optimization algorithm is presented to solve weight optimization of truss structures with continuous variables. The Colliding Bodies Optimization (CBO) is an algorithm based on one-dimensional collisions between two bodies, where each agent solution is modeled as the body. After a collision of two moving bodies, having specified masses and velocities, these are separated and moved to new positions with new velocities. This process is repeated until a termination criterion is satisfied and the optimum CB is found. Comparison of the results of the CBO with those of some previous studies, demonstrate its capability in solving the present optimization problems.

Multiobjective Topology Optimization of Truss Structures for Assembly Devices Using Intelligent Swarm Techniques

The continuously growing variety of cars as well as increasingly shorter development periods necessitate new intelligent methods for supporting the planning and design process of production equipment, which is still based on experience and very time consuming at the moment. To solve these problems a new method for topology optimization of truss structures with objectives “lightweight construction”, “maximum stiffness” and “minimal costs” has been developed. Starting with a bionic preprocessing, ant colony optimization is used to detect suitable areas for truss structure in a given design space. After this particle swarm optimization determines positions of the truss structure nodes. Stochastic influences (e.g. sum and direction of the forces) are taken into the optimization method.

Chaotic biogeography algorithm for size and shape optimization of truss structures with frequency constraints

Size and shape optimization of truss structures with natural frequency constraints is inherently nonlinear dynamic optimization problem with several local optima. Therefore theoptimization method should be sagacious enough to avoid being trapped in local optima and in this way to reduce premature convergence. To address this problem, we develop a Chaotic Biogeography-Based Optimization (CBBO) algorithm which combines the chaos theory and the biogeography-based optimization (BBO) to achieve an ecient optimization method. In this method, new chaotic migration and mutation operators are proposed to enhance the exploration ability of BBO. The performance of the method is demonstrated through five benchmark design examples with size and shape variables associated by multiply frequency constraints. The results show the eciency and robustness of proposed method and in most cases, CBBO finds a relatively lighter structural weight than those previously reported results in the literature.

The stiffness spreading method for layout optimization of truss structures

A novel optimization method, stiffness spreading method (SSM), is proposed for layout optimization of truss structures. In this method, stiffness matrices of the bar elements in a truss structure are represented by a set of equivalent stiffness matrices which are embedded in a weak background mesh. When the proposed method is used, it is unnecessary for the bar elements in a truss structure to be connected to each other during the optimization process, and each of the bar elements can move independently in the design domain to form an optimized design. Another feature of the method is that the sensitivity analysis can be done analytically, making gradient based optimization algorithms applicable in the solution. This method realizes the size, shape and topology design optimization of truss structures simultaneously and allows for more flexibility in topology change. Numerical examples illustrate the feasibility and effectiveness of the proposed method.

Multi-Objective Heuristic Computation Applied to Architectural and Structural Design: A Review

Heuristic computation techniques have been used in a wide range of fields, demonstrating their capacity to solve highly complex optimization problems. This article presents the most common techniques and their extension into the multi-objective optimization field, and emphasizes in the application of them in structural and architectural design by presenting examples within topics like: topological, shape and dimensional optimization of truss structures, roof optimization for sunlight conditions and area minimization, grid structures, façade design, life cycle cost and environmental impact, energy efficiency and construction costs, morphogenetic structural optimization for shell structures, acoustical optimization, evolutionary architectural design, architectural layout design optimization, RC frames optimization, and land use zoning, within others. Finally, the conclusion leads to the recognition of heuristic computation not only as an optimization tool, but also as an important component of a design methodology for creating innovative, creative, efficient, well performing, and aesthetically pleasant architectural/engineering objects.

A bi-level hierarchical method for shape and member sizing optimization of steel truss structures

This paper describes a new bi-level hierarchical method for optimizing the shape and member sizes of both determinate and indeterminate truss structures. The method utilizes a unique combination of algorithms that are organized hierarchically: the Fully Constrained Design (FCD) method for discrete sizing optimization is nested within SEQOPT, a gradient-based optimization method that operates on continuous shape variables. We benchmarked the method against several existing techniques using numerical examples and found that it compared favorably in terms of solution quality and computational efficiency. We also present a successful industry application of the method to demonstrate its practical benefits.

Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm

In this paper, size and shape optimization of truss structures is performed using an efficient hybrid method. This algorithm uses a particle swarm strategy and ray optimizer, and utilizes additional harmony search for a better exploitation. Here, multiple frequency constraints are considered making the optimization a highly nonlinear problem. Some basic benchmark problems are solved by this hybrid method, and the numerical results demonstrate the efficiency and robustness of this method compared to other mathematical and heuristic algorithms.

하모니 서치 알고리즘과 고유진동수 제약조건에 의한 트러스의 단면과 형상 최적설계

We present the optimum design for the cross-sectional(sizing) and shape optimization of truss structures with natural frequency constraints. The optimum design method used in this paper employs continuous design variables and the Harmony Search Algorithm(HSA). HSA is a meta-heuristic search method for global optimization problems. In this paper, HSA uses the method of random number selection in an update process, along with penalty parameters, to construct the initial harmony memory in order to improve the fitness in the initial and update processes. In examples, 10-bar and 72-bar trusses are optimized for sizing, and 37-bar bridge type truss and 52-bar(like dome) for sizing and shape. Four typical truss optimization examples are employed to demonstrate the availability of HSA for finding the minimum weight optimum truss with multiple natural frequency constraints.

Chaotic swarming of particles: A new method for size optimization of truss structures

A new combination of swarm intelligence and chaos theory is presented for optimal design of truss structures. Here the tendency to form swarms appearing in many different organisms and chaos theory has been the source of inspiration, and the algorithm is called chaotic swarming of particles (CSP). This method is a kind of multi-phase optimization technique which employs chaos theory in two phases, in the first phase it controls the parameter values of the particle swarm optimization (CPVPSO) and the second phase is utilized for local search (CLSPSO). Some truss structures are optimized using the CSP algorithm, and the results are compared to those of the other meta-heuristic algorithms showing the effectiveness of the new method.

IMPROVED PSO ALGORITHM FOR SHAPE AND SIZING OPTIMIZATION OF TRUSS STRUCTURE

In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO) algorithm and provide an effective method for shape and sizing optimization of truss structure, an improved PSO was proposed. The random direction method was employed to produce high-quality initial population, the fuzzy system was applied in the dynamic adaptive adjustment of parameters of the PSO, and the Metropolis criteria were used to improve the performance of PSO. Then, the improved PSO was introduced to the truss structure shape and sizing optimization design. Engineering practice and comparison with the other optimization algorithms show that the algorithm has good convergence and global searching capability. The study provides a promising algorithm for the structural optimization.

Optimal Design of CNC Crushing Machine Steady Based on Genetic Algorithm

The Genetic Algorithms In engineering structure optimization design includes Truss Structure optimization, Shape and topology optimization, Composite materials optimization, layout optimization, Multi-Objective Optimization. This paper combined with engineering background , Selecting the Pressing gear of CNC crushing Machine Steady as starting point for Optimization modeling .Analyzing the simple conditions theoretical physical model of CNC Crushing Machine Steady, Reasonably selected design variables ,using Conventional Methods and genetic algorithms to optimize the Steady ,obtainning every iterative step relevant data under the two methods, and analyzing the results ,analysis the accuracy of the optimize results through the stress and displacement map .

Truss Structural Optimization Based on NLPQL and MIGA

Based on the minimum mass as optimal objective function, it sets up an FE model of the truss structure. The Multi-Island Genetic Algorithm (MIGA) and NLPQL are adopted as the optimization policy. Experimented with a truss walking platform as an example the validity of the methods was proved. The results showed that the NLPQL can get better optimal solutions of structure and reduce the mass effectively. It has important theoretical value and practical engineering significance.

Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization

The main contribution of the present paper is to propose an efficient hybrid optimization algorithm for layout optimization of truss structures. To achieve this, computational merits of the cellular automata (CA) and the particle swarm optimization (PSO) are integrated. In the proposed hybrid algorithm a CA-based mechanism is utilized as the velocity updating equation of the particles in the framework of the sequential unconstrained minimization techniques and therefore it is denoted as sequential cellular PSO (SCPSO). The numerical results demonstrate that SCPSO not only converges to better solutions but also provides faster convergence rate in comparison with other algorithms.