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Purpose This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables. The PSOGA is an efficient hybridized algorithm to solve optimization problems. Design/methodology/approach These algorithms have shown outstanding performance in solving optimization problems with continuous variables. The PSO conceptually models the social behavior of birds, in which individual birds exchange information about their position, velocity and fitness. The behavior of a flock is influencing the probability of migration to other regions with high fitness. The GAs procedure is based on the mechanism of natural selection. The present study uses mutation, random selection and reproduction to reach the best genetic algorithm by the operators of natural genetics. Thus, only identical chromosomes or particles can be converged. Findings In this research, using the idea of hybridization PSO and GA algorithms are hybridized and a new meta-heuristic algorithm is developed to minimize the space trusses with continuous design variables. To showing the efficiency and robustness of the new algorithm, several benchmark problems are solved and compared with other researchers. Originality/value The results indicate that the hybrid PSO algorithm improved in both exploration and exploitation. The PSO algorithm can be used to minimize the weight of structural problems under stress and displacement constraints.

Seismic safety of super tall buildings is of paramount importance, since if they get damaged, their repair is very costly on the one hand, and their service interruption requires a substitute space for the occupants, which is very difficult, if not impossible, on the other. Regarding these facts, in seismic design of super tall buildings the desired performance level (PL) is immediate occupancy (IO), and it is obvious that achieving this PL is not possible with conventional seismic design procedures. On this basis, in this paper, the efficiency of using specific energy absorbing stories (SEASs) in improvement of seismic behavior of a set of super tall steel buildings with 50, 60, 70 and 80 stories, with tubular frames combined with X-bracing, as their lateral load bearing system, has been investigated. To create SEASs in each building, in every other few stories the bracing elements have been substituted by dampers, as the additional energy absorption sources. The optimal number and the locations of SEASs in each case have been obtained by an optimization technique based on combination of crossover and mutation. Numerical results show that by considering SEASs in almost one tenth of the buildings’ stories, an optimal design is achieved in each case, and that the optimal use of the SEASs reduces the maximum drifts more than 50% in all considered buildings, resulting in a remarkably higher PL, comparing to that of the buildings designed based on conventional methods.

This research presents a numerical method to design a two-dimensional auxetic microstructure with negative Poisson’s ratios. The method is established by combining the finite element method (FEM) with two different optimization procedures called bi-directional evolutionary structural optimization (BESO) and solid isotropic material with penalization (SIMP), respectively. The results show that by choosing any of the two methods of optimization (FEM and BESO or FEM and SIMP), each with a different objective function, it is possible to create microstructures that show negative Poisson’s ratio. In addition, there is a significant resemblance between the results of both optimization methodologies. The final part of this study is to verify the outcome of optimization, both numerically and experimentally using three-dimensional (3D) printing techniques.

In this study, a new approach based on the polynomial-type extrapolation methods is applied to the approximate structural reanalysis under multiple and large changes in the initial design. In this approach, the sequences of approximate displacement of the modified structure are constructed by using a fixed-point iteration method. These sequences are then further analyzed by two polynomial-type vector extrapolation methods to find the approximate response of the modified structure more accurately, namely, the minimal polynomial extrapolation (MPE) and the reduced rank extrapolation (RRE). Based on a single initial design, the MPE and RRE methods approximate the displacement vector of the modified structure by solving a least-squares problem which is much smaller than the original system of equations of the exact analysis. The accuracy and efficiency of this reanalysis approach is evaluated on three large scale structural reanalysis problems under multiple and large changes in their initial designs. The obtained reanalysis results demonstrate that the MPE and RRE methods not only yield accurate results, but also are computationally efficient.

A new meta-heuristic algorithm is presented for optimum design of engineering problems. This algorithm is inspired by the natural behavior of the forests against the rapidly changing environment. This phenomenon is combined with natural regeneration of forests, and a simple but efficient optimization technique is developed which is called natural forest regeneration (NFR). Some well-studied benchmark optimization problems are investigated, and the results of the NFR are compared with those of some previously published algorithms.

In this paper the recently developed nature inspired metaheuristic algorithm is utilized for optimum design of truss structures with continuous and discrete variables. This algorithm is inspired by the natural process happening in the forests with the rapidly change of environment and their natural regeneration. Based on this process a simple powerful optimization technique is introduced so-called Natural Forest Regeneration (NFR). Some well-studied benchmark structural problems are investigated with both continuous and discrete sizing variables and the results of the NRF are compared to those of some previously developed algorithms.

This article presents a modified biogeography-based optimization (MBBO) algorithm for optimum design of skeletal structures with discrete variables. The main idea of the biogeography-based optimization (BBO) algorithm is based on the science of biogeography, in which each habitat is a possible solution for the optimization problem in the search space. This algorithm consists of two main operators: migration and mutation. The migration operator helps the habitats to exploit the search space, while the mutation operator guides habitats to escape from the local optimum. To enhance the performance of the standard algorithm, some modifications are made and an MBBO algorithm is presented. The performance of the MBBO algorithm is evaluated by optimizing five benchmark design examples, and the obtained results are compared with other methods in the literature. The numerical results demonstrate that the MBBO algorithm is able to show very competitive results and has merits in finding optimum designs.

The optimal design of a truss structure with dynamic frequency constraints is a highly nonlinear optimization problem with several local optimums in its search space. In this type of structural optimization problems, the optimization methods should have a high capability to escape from the traps of the local optimums in the search space. This paper presents hybrid electromagnetism-like mechanism algorithm and migration strategy (EM---MS) for layout and size optimization of truss structures with multiple frequency constraints. The electromagnetism-like mechanism (EM) algorithm simulates the attraction and repulsion mechanism between the charged particles in the field of the electromagnetism to find optimal solutions, in which each particle is a solution candidate for the optimization problem. In the proposed EM---MS algorithm, two mechanisms are utilized to update the position of particles: modified EM algorithm and a new migration strategy. The modified EM algorithm is proposed to effectively guide the particles toward the region of the global optimum in the search space, and a new migration strategy is used to provide efficient exploitation between the particles. In order to test the performance of the proposed algorithm, this study utilizes five benchmark truss design examples with frequency constraints. The numerical results show that the EM---MS algorithm is an alternative and competitive optimizer for size and layout optimization of truss structures with frequency constraints.

The goal of this paper is to state and evaluate the differences in gap acceptance observations between left lane and right lane change, and experiment overall aggressiveness by the means of right lane change behaviors and use of electrical instruments for reaching this goal, furthermore we use Digital Signal Processing on our controlling cameras to be able to distinguish different behaviours of drivers. Also, in this paper we evaluate the decision making process of drivers, we do this work with use of electrical sensors for accumulating some data and clarifying and processing them and finally with use of cumulative distribution functions of driver lane change behaviours from the observed field data. These experiments are performed for drivers using I-20 in Grand Prairie, Texas with the roadside controlling cameras and some other electronical controlling instruments which were amounted near the intersection of I-20 and Great Southwest Blvd. Our experiments and evaluations demonstrates, that the whole ratio of right lane change observations to left lane change observations was close to 3 to 1.