• Journal of the Korean Society for Railway
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• v.13 no.6
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• pp.601-607
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• 2010

The objective of this paper is to propose a fast and easy Binary Artificial Bee Colony (BABC) heuristic algorithm to optimize NP-hard scheduling problem of railway track maintenance considering real conditions. The optimal or best solutions can be found using proposed BABC within very short or user specified computation time. We can greatly maximize the objective value using this proposed method in 30, 60, 100 and 200 work size railway track maintenance scheduling problems for experiment and analysis.

• 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.

• International Journal of Industrial Entomology and Biomaterials
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• v.37 no.2
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• pp.109-116
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• 2018

We investigated the effects of honey bee (Apis mellifera L.) colony size on the pollination of greenhouse-cultivated watermelon grown under the forcing cultivation system. The highest pollination activity of bees was observed ($14.3{\pm}5.0$ honey bees/day) when the bee colony size was 10,000 followed by 7,500 and 5,000 honey bees. There was a positive correlation between the bee colony size and pollination activity (R = 0.262) but insignificant difference in fruit set with different honey bee colony sizes (88%-91%). Evaluation of physical properties revealed that the weight and shape of watermelon were also not significantly different among different colony sizes. However, larger the bee colony size, higher the number of seeds were fertilized and rate of seed fertilization (p > 0.05). Number of seeds and content of sugar were negatively correlated (R = -0.714). Fertilized seeds showed a significant increase in mealy flesh, which has a negative effect on fruit quality, compared with that of the unfertilized seeds. Overall, we found that a colony size of 5,000 honey bees was the most effective for the pollination of watermelon grown under forcing cultivation. A comparison of the effects of bee pollination with those of artificial pollination suggested that artificial pollination can be effectively replaced by bee pollination in the forcing cultivation of watermelon, because fruit set, weight, and shape by bee pollination were similar to those achieved by artificial pollination.

• Korean Management Science Review
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• v.28 no.1
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• pp.43-52
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• 2011

The basic objective of broadcast scheduling is to get the smallest length TDMA frame, where many nodes are allowed to transmit simultaneously in a single time slot in a conflict-free manner. The secondary objective is to maximize the number of such transmissions for maximum use of the channel. An Artificial Bee Colony (ABC) with ranking strategy is proposed in this paper for the broadcast scheduling problem. Our proposed method is very efficient for generating initial and neighbor feasible solutions. We can get the best number of time slots and transmission utilization comparing to previous researches.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1191-1202
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• 2010

The objective of this paper is to propose a Binary Artificial Bee Colony (BABC) to obtain the best/optimal solution for NP-hard scheduling problem of railway track maintenance. We can greatly maximize the objective value using proposed BABC within limited computation time. The proposed BABC mechanism is very efficient to find the best solution because of employing fewer control parameters.

• 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.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.433-439
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• 2019

Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability. The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other. However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental results on a series of benchmarks algorithms demonstrate its effectiveness.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.