Sizing and layout optimization of truss structures with artificial bee colony algorithm

Effect of Seismic Isolation with Triple Friction Pendulum Isolator Device on Weight Optimization of Steel Plane Frames

“…(8) [7]. (8) Here, subscript i represents any constraint function, NC is the total number of constraint functions in the optimization problem. In the study, the fitness value of the candidate solution (Fit) is inversely proportional to penalized weight and is formulated in Eq.…”

“…Many metaheuristic optimization algorithms such as Genetic algorithm, Archimedean optimization algorithm, and Crow search algorithm have been developed and successfully applied to complex optimization problem so far, and artificial bee colony algorithm (ABC) is one of these algorithms. ABC algorithm which simulates the foraging behavior of honey bees, performed well in structural optimization problems for optimal sizing of truss and frame structures [7][8]. In literature, there are many optimization studies related to seismic isolated structures.…”

Generation of eco‐friendly design for post‐tensioned axially symmetric reinforced concrete cylindrical walls by minimizing of CO2 emission

“…Here, based on the scope of the current study, to make it clear to them for structural engineering applications, various optimization processes containing previous and recently-developed metaheuristic algorithms are expressed. As an example to these, to generate the best structural design for trusses in the direction of providing size, geometry, topology optimization, and so on, adaptive dimensional search (ADS), [22] harmony search (HS), [23] teaching-learning based optimization (TLBO), [24] water cycle algorithm (WCA), [25] Jaya algorithm (JA), [26] artificial bee colony (ABC) algorithm, [27] and nine different metaheuristic approaches [28] are utilized. Also, in several pieces of research, charged system search (CSS), [29] HS, [30] differential evolution (DE), [31] flower pollination algorithm (FPA), [32,33] genetic algorithm (GA), [34] crow search algorithm (CSA), whale optimization algorithm (WOA), and gray wolf optimization (GWO), [35] FPA, TLBO, and also JA [36] are preferred to determine of various special mechanical properties with the aim of best adjusting of structural control systems (such as providing of sufficient damping).…”

Size/Layout Optimization of Truss Structures Using Vibrating Particles System Meta-heuristic Algorithm and its Improved Version

“…Different meta-heuristic algorithms have been used by various researchers to optimize the size and layout of the structure. For instance, Wu and Chow [15] considered the sections and nodal coordinates as a discrete and continuous variables to optimize space trusses by GA algorithm, Hasançebi and Erbatur [16] used "annealing perturbation" and "adaptive reduction of the design space" method to improve GA for mass minimization of space trusses, Hasançebi and Erbatur [17] used Simulated Annealing (SA) algorithm, Kaveh and Kalatjari [18] used the force method and genetic algorithm to minimize the weight of the truss structure, Tang et al [19] optimized size, shape and topology of trusses using the improved Genetic Algorithm (GA), Rahami et al [20] employed energy and force method and Genetic algorithm (GA) for optimize the size and layout of the truss structures, Kazemzadeh Azad et al [21] suggested a Mutation-Based Real-Coded Genetic Algorithm (MBRCGA) for minimizing the weight of truss, Miguel et al [22] presented a single-stage Fireflybased Algorithm (FA) to study simultaneous size, shape and topology optimization for trusses, Gholizadeh [23] proposed a hybrid of Cellular Automata (CA) and the Particle Swarm Optimization (PSO) algorithm to solve size and layout optimization of truss structures, Mortazavi et al [24] utilized integrated Particle Swarm Optimizer (iPSO) algorithm for sizing and shape optimization of planar and spatial truss structures, Panagant and Bureerat [25] suggested Fully Stressed Design-Grey Wolf-Adaptive Differential Evolution (FSD-GWADE), Kaveh and Zaerreza [13] utilized shuffled shepherd optimization algorithm (SSOA), Jawad et al [26] used Artificial Bee Colony (ABC) algorithm, Kaveh et al [27] studied layout optimization of planar braced frames by using Colliding Bodies Optimization (CBO) and Colliding Bodies Optimization-Modified Dolphin Monitoring (CBO-MDM).…”

“…(26) as the decremental and incremental parameters, respectively which lead to more exploration-less exploitation in the initial iteration loops of the algorithm and less exploration-more exploitation in the final iteration loops of the algorithm. (26) According to the second step, in the initial iteration loops of the algorithm, the effect of good and bad particles decreases and increases, respectively, and in the final iteration loops of the algorithm, the effect of good and bad particles increases and decreases, respectively. Also, by adding the weight of the selected particle (m IP ) to the weight of the OHB particle (m OHB ), the convergence speed is increased, and to avoid being in the local optima, we reduce the probability of this effect by considering the γ reduction coefficient.…”