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Literatura académica sobre el tema "ABC-Problem"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "ABC-Problem"

1

Chow, Ho Yoong, Sulaiman Hasan, and Salleh Ahmad Bareduan. "Basic Concept of Implementing Artificial Bee Colony (ABC) System in Flow Shop Scheduling." Applied Mechanics and Materials 315 (April 2013): 385–88. http://dx.doi.org/10.4028/www.scientific.net/amm.315.385.

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Flow shop scheduling is a common operational problem in a production system. Effective flow shop scheduling can help the company to improve the management system, hence increase income. Artificial Bee Colony (ABC) is a system that is widely used for scheduling optimization in a production system since 2005. However, the fundamental ABC system uses a heuristic approach to obtain an optimum solution which may not be the optimum solution at all. The ABC system is tested on the speed to obtain the optimum solution for a flowshop scheduling problem and measures the applicability of the schedule in terms of makespan. A simple model of ABC algorithm was developed to identify the effectiveness of the ABC for solving flow shop scheduling problem compared to other established methods. Result shows the ABC model is capable of producing best makespan in flow shop problem tested.
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2

Salhi, Souheil, Djemai Naimi, Ahmed Salhi, Saleh Abujarad, and Abdelouahab Necira. "A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1825. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1825-1837.

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Optimal reactive power dispatch (ORPD) is an important task for achieving more economical, secure and stable state of the electrical power system. It is expressed as a complex optimization problem where many meta-heuristic techniques have been proposed to overcome various complexities in solving ORPD problem. A meta-heuristic search mechanism is characterized by exploration and exploitation of the search space. The balance between these two characteristics is a challenging problem to attain the best solution quality. The artificial bee colony (ABC) algorithm as a reputed meta-heuristic has proved its goodness at exploration and weakness at exploitation where the enhancement of the basic ABC version becomes necessary. Salp swarm algorithm (SSA) is a newly developed swarm-based meta-heuristic, which has the best local search capability by using the best global solution in each iteration to discover promising solutions. In this paper, a novel hybrid approach-based ABC and SSA algorithms (ABC-SSA) is that developed to enhance the exploitation capability of the ABC algorithm using SSA and applied for solving ORPD problem. The efficiency of ABC-SSA is investigated using two standard test systems IEEE-30 and IEEE-300 buses, and that by considering the famous objective functions in ORPD problem.
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3

Kim, Junghoon, Kaiyu Feng, Gao Cong, Diwen Zhu, Wenyuan Yu, and Chunyan Miao. "ABC." Proceedings of the VLDB Endowment 15, no. 10 (2022): 2134–47. http://dx.doi.org/10.14778/3547305.3547318.

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Finding a set of co-clusters in a bipartite network is a fundamental and important problem. In this paper, we present the Attributed Bipartite Co-clustering (ABC) problem which unifies two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. To the best of our knowledge, this is the first work to find co-clusters while considering the attribute cohesiveness. We prove that ABC is NP-hard and is not in APX, unless P=NP. We propose three algorithms: (1) a top-down algorithm; (2) a bottom-up algorithm; (3) a group matching algorithm. Extensive experimental results on real-world attributed bipartite networks demonstrate the efficiency and effectiveness of our algorithms.
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4

Ying Xiao and Yilong Lu. "Combination of PML and ABC for scattering problem." IEEE Transactions on Magnetics 37, no. 5 (2001): 3510–13. http://dx.doi.org/10.1109/20.952649.

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5

Yang, Weihong, and Zhili Pei. "Hybrid ABC/PSO to solve travelling salesman problem." International Journal of Computing Science and Mathematics 4, no. 3 (2013): 214. http://dx.doi.org/10.1504/ijcsm.2013.057246.

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6

Odanaka, T., and T. Tanaka. "On ABC analysis of multi-item inventory problem." Mathematical Modelling 8 (1987): 725–28. http://dx.doi.org/10.1016/0270-0255(87)90678-6.

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7

Khan, Indadul, Manas Kumar Maiti, and Krishnendu Basuli. "Multi-objective traveling salesman problem: an ABC approach." Applied Intelligence 50, no. 11 (2020): 3942–60. http://dx.doi.org/10.1007/s10489-020-01713-4.

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8

He, Xinggang, and Haixiong Li. "On the abc-problem in Weyl-Heisenberg frames." Czechoslovak Mathematical Journal 64, no. 2 (2014): 447–58. http://dx.doi.org/10.1007/s10587-014-0111-z.

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9

Sharma, Harish, Jagdish Chand Bansal, K. V. Arya, and Kusum Deep. "Dynamic Swarm Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 3, no. 4 (2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.

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Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.
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10

Alaidi, A. H., S. D. Chen, and Υ. Weng Leong. "Artificial Bee Colony with Crossover Operations for Discrete Problems." Engineering, Technology & Applied Science Research 12, no. 6 (2022): 9510–14. http://dx.doi.org/10.48084/etasr.5250.

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The Artificial Bee Colony (ABC) is an algorithm designed to solve continuous problems. ABC has been proven to be more effective than other biological-inspired algorithms. However, it is needed to modify its functionality in order to solve a discrete problem. In this work, a natural modification to the original ABC is made to make it able to solve discrete problems. Six neighborhood operators are proposed to simulate the original behavior of ABC. Moreover, several Traveling Salesman Problem Library (TSPLIB) problems were used to examine the proposed method. The results of the proposed method are promising.
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