Hypotrochoid spiral optimization approach for sizing and layout optimization of truss structures with multiple frequency constraints

Engineering with Computers - The primary aim of this article is to present a new improved version of the spiral optimization algorithm (SPO) for shape and size optimization of truss structures...     

Exporting water wave optimization concepts to modified simulated annealing algorithm for size optimization of truss structures with natural frequency constraints

This paper proposes an improved version of a recently proposed modified simulated annealing algorithm (MSAA) named as an improved MSAA (I-MSAA) to tackle the size optimization of truss structures with frequency constraint. This kind of problem is problematic because its feasible region is non-convex while the boundaries are highly non-linear. The main motivation is to improve the exploitative behavior of MSAA, taking concept from water wave optimization metaheuristic (WWO). An interesting concept of WWO is its breaking operation. Thirty functions extracted from the CEC2014 test suite and four benchmark truss optimization problems with frequency constraints are explored for the validity of the proposed algorithm. Numerical results indicate that I-MSAA is more reliable, stable and efficient than those found by other existing metaheuristics in the literature.

     

An improved electromagnetism-like mechanism algorithm for constrained optimization

Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some modifications are made for improving the performance of EM algorithm. The process of calculating the total force is simplified and an improved total force formula is adopted to accelerate the searching for optimal solution. In order to improve the accuracy of EM algorithm, a parameter called as move probability is introduced into the move formula where an elitist strategy is also adopted. And then, to handle the constraints, the feasibility and dominance rules are introduced and the corresponding charge formula is used for biasing feasible solutions over infeasible ones. Finally, 13 classical functions, three engineering design problems and 22 benchmark functions in CEC’06 are tested to illustrate the performance of proposed algorithm. Numerical results show that, compared with other versions of EM algorithm and other state-of-art algorithms, the improved EM algorithm has the advantage of higher accuracy and efficiency for constrained optimization problems.     

An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is well known as a highly nonlinear and complex optimization problem with many local optimum solutions. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this work, a novel differential evolution (DE) is proposed to solve the shape and size optimization problems for truss structures with frequency constraints. The proposed method, called ReDE, is a new version of the DE algorithm with two improvements. Firstly, the roulette wheel selection is employed to choose members for the mutation phase instead of random selection as in the conventional DE. Secondly, an elitist selection technique is applied to the selection phase instead of basic selection to improve the convergence speed of the method. The efficiency and reliability of the proposed method are demonstrated through five numerical examples. Numerical results reveal that the proposed algorithm outperforms many optimization methods in the literature.

     

Integrated size and topology optimization of skeletal structures with exact frequency constraints

The present paper studies the integrated size and topology optimization of skeletal structures under natural frequency constraints. It is found that, unlike the conventional compliance-oriented topology optimization problems, the considered problem may be strongly singular in the sense that the corresponding feasible domain may be disconnected and the global optimal solutions are often located at the tips of some separated low dimensional sub-domains when the cross-sectional areas of the structural components are used as design variables. As in the case of stress-constrained topology optimization, this unpleasant behavior may prevent the gradient-based numerical optimization algorithms from finding the true optimal topologies. To overcome the difficulties posed by the strongly singular optima, some particular forms of area/moment of inertia-density interpolation schemes, which can restore the connectedness of the feasible domain, are proposed. Based on the proposed optimization model, the probability of finding the strongly singular optimum with gradient-based algorithms can be increased. Numerical examples demonstrate the effectiveness of the proposed approach.     

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.

     

Improved genetic algorithm with two-level approximation using shape sensitivities for truss layout optimization

Truss layout optimization is a procedure for optimizing truss structures under the combined influence of size, shape and topology variables. This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) that uses continuous shape variables and shape sensitivities to minimize the weight of trusses under static or dynamic constraints. A uniform optimization model including continuous size/shape variables and discrete topology variables is established. With the introduction of shape sensitivities, the first-level approximations of constraint functions are constructed with respect to shape/topology/size variables. This explicit problem is solved by implementation of a real-coded GA for continuous shape variables and binary-coded GA for 0/1 topology variables. Acceleration techniques are used to overcome the convergence difficulty of the mixed-coded GA. When calculating the fitness value of each member in the current generation, a second-level approximation method is embedded to optimize the continuous size variables effectively. The results of numerical examples show that the usage of continuous shape variables and shape sensitivities improves the algorithm performance significantly.     

Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints

Structural optimization with frequency constraints is a challenging class of optimization problems characterized by highly non-linear and non-convex search spaces. When using a meta-heuristic algorithm to solve a problem of this kind, exploration/exploitation balance is a key feature to control the performance of the algorithm. An excessively exploitative algorithm might focus on certain areas of the search space ignoring the others. On the other hand, an algorithm that is too explorative overlooks high quality solutions as a result of not performing adequate local search.This paper compares nine multi-agent meta-heuristic algorithms for sizing and layout optimization of truss structures with frequency constraints. The variation of the diversity index during the optimization history is analyzed in order to inspect exploration/exploitation properties of each algorithm. It appears that there is a significant relationship between the algorithm efficiency and the evolution of the diversity index.     

Discrete optimization of geometrically nonlinear truss structures under stability constraints

The paper deals with discrete optimization of elastic trusses with geometrical nonlinear behaviour and constraints on stability. The problem consists of minimizing the weight and determining the optimal member distribution so that the external load does not cause a loss of stability of the structure. Member cross-sections are selected from a catalogue of available sections. Element stresses, elment stability and global structural stability constraints are considered. A controlled enumeration method according to the increasing value of the objective function is applied. Shallow space trusses are numerically analysed.     

Reinforcement layout design for concrete structures based on continuum damage and truss topology optimization

This article presents a new procedure for the layout design of reinforcement in concrete structures. Concrete is represented by a gradient-enhanced continuum damage model with strain-softening and reinforcement is modeled as elastic bars that are embedded into the concrete domain. Adjoint sensitivity analysis is derived in complete consistency with respect to path-dependency and the nonlocal model. Classical truss topology optimization based on the ground structure approach is applied to determine the optimal topology and cross-sections of the reinforcement bars. This approach facilitates a fully digital work flow that can be highly effective, especially for the design of complex structures. Several test cases involving two- and three-dimensional concrete structures illustrate the capabilities of the proposed procedure.     

Sizing optimization of truss structures using flower pollination algorithm

The recently developed flower pollination algorithm is used to minimize the weight of truss structures, including sizing design variables. The new algorithm can efficiently combine local and global searches, inspired by cross-pollination and self-pollination of flowering plants, respectively. Furthermore, it implements an iterative constraint handling strategy where trial designs are accepted or rejected based on the allowed amount of constraint violation that is progressively reduced as the search process approaches the optimum. This strategy aims to obtain always feasible optimized designs. The new algorithm is tested using three classical sizing optimization problems of 2D and 3D truss structures. Optimization results show that the proposed method is competitive with other state-of-the-art metaheuristic algorithms presented in the literature.     

A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system

The layout design problem is one of the most important issues for manufacturing system design and control. A revised electromagnetism-like mechanism (REM) is proposed in this paper for the layout design of reconfigurable manufacturing systems utilizing automated guided vehicle. First, the formal model considering both loaded and empty flows is given. Then the REM is developed to solve the proposed model. In the REM, particles are encoded discretely. The charge of a particle is calculated according the total material handling cost of the particle. In the local search procedure, variable neighbourhood search strategy based on Hamming distance is adopted. In the moving procedure, the particles are moved according to the ordering of each element. To verify the effect of the proposed method, several computation cases are carried out. The computation results show that the proposed method is able to get optimal solutions for small scale problems and near optimal solutions within limited computation time for large scale problems. This indicates that the proposed method is effective and efficient.     

Growth method for size,topology, and geometry optimization of truss structures

The problem of optimally designing the topology of plane trusses has, in most cases, been dealt with as a size problem in which members are eliminated when their size tends to zero. This article presents a novel growth method for the optimal design in a sequential manner of size, geometry, and topology of plane trusses without the need of a ground structure. The method has been applied to single load case problems with stress and size constraints. It works sequentially by adding new joints and members optimally, requiring five basic steps: (1) domain specification, (2) topology and size optimization, (3) geometry optimization, (4) optimality verification, and (5) topology growth. To demonstrate the proposed growth method, three examples were carried out: Michell cantilever, Messerschmidt–Bölkow–Blohm beam, and Michell cantilever with fixed circular boundary. The results obtained with the proposed growth method agree perfectly with the analytical solutions. A Windows XP program, which demonstrates the method, can be downloaded from http://www.upct.es/~deyc/software/tto/.     

Efficient size and shape optimization of truss structures subject to stress and local buckling constraints using sequential linear programming

The advance in digital fabrication technologies and additive manufacturing allows for the fabrication of complex truss structure designs but at the same time posing challenging structural optimization problems to capitalize on this new design freedom. In response to this, an iterative approach in which Sequential Linear Programming (SLP) is used to simultaneously solve a size and shape optimization sub-problem subject to local stress and Euler buckling constraints is proposed in this work. To accomplish this, a first order Taylor expansion for the nodal movement and the buckling constraint is derived to conform to the SLP problem formulation. At each iteration a post-processing step is initiated to map a design vector to the exact buckling constraint boundary in order to facilitate the overall efficiency. The method is verified against an exact non-linear optimization problem formulation on a range of benchmark examples obtained from the literature. The results show that the proposed method produces optimized designs that are either close or identical to the solutions obtained by the non-linear problem formulation while significantly decreasing the computational time. This enables more efficient size and shape optimization of truss structures considering practical engineering constraints.     

An evolutionary approach for layout optimization of a three-dimensional truss

Layout optimization is defined as the simultaneous optimization of three aspects, i.e. topology, size and geometry. In this paper, a new approach based on evolutionary genetic search is employed to find the optimal design. The proposed approach departs from standard Genetic Algorithms (GA) in that it does not use binary strings in the representation of the possible solutions. The approach is applied to a 2-D benchmark example and a well-known space truss example. This 25-bar-truss is solved by using the proposed approach, with good results. The effect of removing the restriction of area grouping and the free location of joints and supports in the 25-bar truss are also investigated, with and without symmetry. Major drawbacks in finding the optimal solution by using the ground structure approach are overcome in the proposed method.     

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.     

Minimum weight shape and size optimization of truss structures made of uncertain materials

Truss structures are optimized with respect to minimum weight with constraints on the value of some displacement and on the member stresses. The truss is considered made of an uncertain material, i.e. the value of the material constants are not known in a deterministic way, and each member may then exhibit a different value of stiffness, within a limited range of variation. The optimization must be done so that optimal solutions remain feasible for each value that the material constants may take for the considered uncertainty. In the present work a nonprobabilistic approach to uncertainty is used, and a variation of the material moduli with a, probabilistically speaking, uniform distribution over a convex and linearly bounded domain is considered. The two-step method is used to include the uncertainty within the optimization, where a diagonal quadratic approximation is used for the Objective function and the constraints. Solutions for some of the most classical truss examples are found and compared with those obtained using nominal values of material constants.     

Improved genetic algorithm with two-level approximation for truss topology optimization

Truss topology optimization using Genetic Algorithms (GAs) usually requires large computational cost, especially for large-scale problems. To decrease the structural analyses, a GA with a Two-level Approximation (GATA) was proposed in a previous work, and showed good computational efficiency with less structural analyses. However, this optimization method easily converges to sub-optimum points, resulting in a poor ability to search for a global optimum. Therefore, to address this problem, we propose an Improved GA with a Two-level Approximation (IGATA) which includes several modifications to the approximation function and simple GA developed previously. A Branched Multi-point Approximation (BMA) function, which is efficient and without singularity, is introduced to construct a first-level approximation problem. A modified Lemonge penalty function is adopted for the fitness calculation, while an Elite Selection Strategy (ESS) is proposed to improve the quality of the initial points. The results of numerical examples confirm the lower computational cost of the algorithm incorporating these modifications. Numerous numerical experiments show good reliability of the IGATA given appropriate values for the considered parameters.