• Structural Engineering and Mechanics
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• v.44 no.4
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• pp.501-519
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• 2012

Truss weight is one of the most important factors in the cost of construction that should be reduced. Different methods have been proposed to optimize the weight of trusses. The artificial bee colony algorithm has been proposed recently. This algorithm selects the lightest section from a list of available profiles that satisfy the existing provisions in the design codes and specifications. An important issue in optimization algorithms is how to impose constraints. In this paper, the artificial bee colony algorithm is used for the discrete optimization of trusses. The fly-back mechanism is chosen to impose constraints. Finally, with some basic examples that have been introduced in similar articles, the performance of this algorithm is tested using the fly-back mechanism. The results indicate that the rate of convergence and the accuracy are optimized in comparison with other methods.

• Advances in Energy Research
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• v.7 no.1
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• pp.35-52
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• 2020

The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.

• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.755-765
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• 2014

In this present work, Artificial Bee Colony Algorithm (ABCA) is used to optimize the stacking sequences of simply supported antisymmetric laminated composite plates with criticial buckling load as the objective functions. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. In order to perform the optimization based on the ABCA, a special code is written in MATLAB software environment. Several numerical examples are presented to illustrate this optimization algorithm for different plate aspect ratios, number of layers and load ratios.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Steel and Composite Structures
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• v.19 no.2
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• pp.349-368
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• 2015

This paper presents a new algorithm to find the optimal distribution of steel diagonal braces (SDB) using artificial bee colony optimization technique. The four different objective functions are employed based on the transfer function amplitude of; the top displacement, the top absolute acceleration, the base shear and the base moment. The stiffness parameter of SDB at each floor level is taken into account as design variables and the sum of the stiffness parameter of the SDB is accepted as an active constraint. An optimization algorithm based on the Artificial Bee Colony (ABC) algorithm is proposed to minimize the objective functions. The proposed ABC algorithm is applied to determine the optimal SDB distribution for planar buildings in order to rehabilitate existing planar steel buildings or to design new steel buildings. Three planar building models are chosen as numerical examples to demonstrate the validity of the proposed method. The optimal SDB designs are compared with a uniform SDB design that uniformly distributes the total stiffness across the structure. The results of the analysis clearly show that each optimal SDB placement, which is determined based on different performance objectives, performs well for its own design aim.

• Structural Engineering and Mechanics
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• v.45 no.5
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• pp.643-654
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• 2013

Optimum cost design of columns subjected to axial force and uniaxial bending moment is presented in this paper. In the formulation of the optimum design problem, the height and width of the column, diameter and number of reinforcement bars are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the column consisting the cost of concrete, steel, and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The Artificial Bee Colony Algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

• Computers and Concrete
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• v.10 no.3
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• pp.295-306
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• 2012

Optimum cost design of a simply supported reinforced concrete beam is presented in this paper. In the formulation of the optimum design problem, the height and width of the beam, and reinforcement steel area are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the beam consisting the cost of concrete, steel and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1209-1224
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• 2014

In Current paper, the voltages of patches optimization are carried out for minimizing the power consumption of piezoelectric patches and maximum vertical displacement of symmetrically FML panels using the modified multi-objective Elitist-Artificial Bee Colony (E-ABC) algorithm. The voltages of patches, panel length/width ratios, ply angles, thickness of metal sheets and edge conditions are chosen as design variables. The classical laminated plate theory (CLPT) is considered to model the transient response of the panel, and numerical results are obtained by the finite element method. The performance of the E-ABC is also compared with the PSO algorithm and shows the good efficiency of the E-ABC algorithm. To check the validity, the transient responses of isotropic and orthotropic panels are compared with those available in the literature and show a good agreement.

• Steel and Composite Structures
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• v.12 no.6
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• pp.505-522
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• 2012

An artificial bee colony (ABC) algorithm is developed for the optimum design of geometrically non-linear steel frames. The ABC is a new swarm intelligence method which simulates the intelligent foraging behaviour of honeybee swarm for solving the optimization problems. Minimum weight design of steel frames is aimed under the strength, displacement and size constraints. The geometric non-linearity of the frame members is taken into account in the optimum design algorithm. The performance of the ABC algorithm is tested on three steel frames taken from literature. The results obtained from the design examples demonstrate that the ABC algorithm could find better designs than other meta-heuristic optimization algorithms in shorter time.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.3
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• pp.279-283
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• 2014

Recently, a topology algorithm based on the artificial bee colony algorithm (ABCA) has been proposed for static and dynamic topology optimization. From the results, the convergence rate of the algorithm was determined to be slightly slow. Therefore, we propose a new search method to improve the convergence rate of the algorithm using a chaotic map. We investigate the effect of the chaotic map on the convergence rate of the algorithm in static and dynamic topology optimization. The chaotic map has been applied to three cases, namely, employ bee search, onlooker bee search, and both employ bee as well as onlooker bee search steps. It is verified that the case in which the logistic function of the chaotic map is applied to both employ bee as well as onlooker bee search steps shows the best dynamic topology optimization, improved by 5.89% compared to ABCA. Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate.