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Journal articles on the topic 'ABC-Problem'

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Published: 25 May 2024

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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

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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 pro

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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

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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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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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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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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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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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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

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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 ar

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Borodulin, R. Yu, and N. O. Lukyanov. "Statement of absorbing Mur boundary conditions of the first order of accuracy for solving problems of electrodynamics by the method of finite differences in the time domain." Radioengineering 8 (2021): 57–68. http://dx.doi.org/10.18127/j00338486-202108-07.

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Problem statement. The accuracy and convergence of calculations for solving problems of electrodynamics by the finite difference method in the time domain significantly depends on the correct choice of parameters and the correct setting of the absorbing boundary conditions (ABC). Two main types of absorbing boundary conditions are known: Mur ABC; Beranger ABC. It is believed that the Mur ABC is less effective at absorbing spherical waves than the Beranger ABC, but they do not require the introduction of additional parameters (the so-called "Beranger fields"), which simplifies the implementatio

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Le Dinh, Luong, Dieu Vo Ngoc, and Pandian Vasant. "Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/159040.

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This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algo

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Lenin, K. "DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 10 (2017): 63–71. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2269.

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Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test sys

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Akbari, Reza, Vahid Zeighami, and Ismail Akbari. "An ABC-Genetic method to solve resource constrained project scheduling problem." Artificial Intelligence Research 1, no. 2 (2012): 185. http://dx.doi.org/10.5430/air.v1n2p185.

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The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as arecently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For thispurpose the ABC is combined with the Genetic Algorithm (GA). At the initial time, the algorithm generates a set ofschedules randomly. The initial solution is evaluated against constraints and the infeasible solutions are resolved tofeasible ones. Then, the initial schedules will be improved iteratively using hybrid method until termination condition ismet. The propose

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Zhang, Yongjie, and Qin Sun. "Conformal PML with vector ABC undersurface for scattering problem." International Journal of Applied Electromagnetics and Mechanics 33, no. 1-2 (2010): 39–45. http://dx.doi.org/10.3233/jae-2010-1094.

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Boote, Stacy K., and David N. Boote. "ABC problem in elementary mathematics education: Arithmetic before comprehension." Journal of Mathematics Teacher Education 21, no. 2 (2016): 99–122. http://dx.doi.org/10.1007/s10857-016-9350-2.

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Dhouib, Saima. "Hybrid Metaheuristic to Optimize Traceability in the Food Industry." International Journal of Strategic Engineering 4, no. 2 (2021): 14–27. http://dx.doi.org/10.4018/ijose.2021070102.

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In this paper, the authors propose a new hybrid metaheuristic to solve the problem of manufacturing batch dispersion. The method consists of inserting the record to record travel algorithm (RRT) in the artificial bee colony (ABC) in order to ensure balance between the diversification and the intensification phases. The new technique is named RRT-ABC, and it starts by launching the standard ABC, and then the onlooker research phase is enriched by the RRT algorithm. So, the main idea of this research work is to solve the NP-hard problem of minimizing the batch dispersion using a novel metaheuris

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Yu, Wenjie, Xunbo Li, Hanbin Cai, Zhi Zeng, and Xiang Li. "An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/3102628.

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The artificial bee colony (ABC) algorithm is a relatively new optimization technique for simulating the honey bee swarms foraging behavior. Due to its simplicity and effectiveness, it has attracted much attention in recent years. However, ABC search equation is good at global search but poor at local search. Some different search equations are developed to tackle this problem, while there is no particular algorithm to substantially attain the best solution for all optimization problems. Therefore, we proposed an improved ABC with a new search equation, which incorporates the global search fact

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ALAM, MD SHAFIUL, MD MONIRUL ISLAM, and KAZUYUKI MURASE. "ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION." International Journal of Computational Intelligence and Applications 13, no. 02 (2014): 1450010. http://dx.doi.org/10.1142/s1469026814500102.

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The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated anne

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Muniyan, Rajeswari, Rajakumar Ramalingam, Sultan S. Alshamrani, et al. "Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem." Mathematics 10, no. 15 (2022): 2576. http://dx.doi.org/10.3390/math10152576.

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The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance th

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Ahgajan, Vian H., Yasir G. Rashid, and Firas Mohammed Tuaimah. "Artificial bee colony algorithm applied to optimal power flow solution incorporating stochastic wind power." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1890. http://dx.doi.org/10.11591/ijpeds.v12.i3.pp1890-1899.

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<span lang="EN-US">This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC al

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Nuruzzaman, Andi, Rwahita Satyawati, and Sri Mardjiati Mei Wulan. "Cross-Cultural Adaptation of Indonesian Version of Activities-Specific Balance Confidence Scale for Elderly in Surabaya." Folia Medica Indonesiana 56, no. 4 (2021): 261. http://dx.doi.org/10.20473/fmi.v56i4.24601.

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Falling is a major health problem and is also a major cause of morbidity and mortality in the elderly. The more serious consequences of falling on the elderly are the increased risk of injury and fear of falling. Activities-Specific Balance Confidence (ABC) scale is one of the assessment instruments for fear of falling. This instrument is used to assess balance confidence in various ambulation activities. The purpose of this study was to carry out the process of adaptation of the cross cultural ABC English questionnaire to ABC Indonesian. This study was a cross sectional study with participant

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Nuruzzaman, Andi, Rwahita Satyawati, and Sri Mardjiati Mei Wulan. "Cross-Cultural Adaptation of Indonesian Version of Activities-Specific Balance Confidence Scale for Elderly in Surabaya." Folia Medica Indonesiana 56, no. 4 (2020): 261. http://dx.doi.org/10.20473/fmi.v56i4.23408.

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Falling is a major health problem and is also a major cause of morbidity and mortality in the elderly. The more serious consequences of falling on the elderly are the increased risk of injury and fear of falling. Activities-Specific Balance Confidence (ABC) scale is one of the assessment instruments for fear of falling. This instrument is used to assess balance confidence in various ambulation activities. The purpose of this study was to carry out the process of adaptation of the cross cultural ABC English questionnaire to ABC Indonesian. This study was a cross sectional study with participant

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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behav

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Gergin, Zeynep, Nükhet Tunçbilek, and Şakir Esnaf. "Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem." International Journal of Operations Research and Information Systems 10, no. 1 (2019): 56–75. http://dx.doi.org/10.4018/ijoris.2019010104.

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In this study, an Artificial Bee Colony (ABC) based clustering algorithm is proposed for solving continuous multiple facility location problems. Unlike the original version applied to multivariate data clustering, the ABC based clustering here solves the two-dimensional clustering. On the other hand, the multiple facility location problem the proposed clustering algorithm deals with is aimed to find site locations for healthcare wastes. After applying ABC based clustering algorithm on test data, a real-world facility location problem is solved for identifying healthcare waste disposal facility

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Chidambaram, Chidambaram, and Heitor Silvério Lopes. "An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching." International Journal of Natural Computing Research 1, no. 2 (2010): 54–70. http://dx.doi.org/10.4018/jncr.2010040104.

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In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results a

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Chandra, Agung, and Aulia Naro. "NATURE INSPIRED METAHEURISTICS COMPARATIVE STUDY TO SOLVE TRAVELING SALESMAN PROBLEM." Journal of Engineering and Management in Industrial System 9, no. 2 (2021): 1–10. http://dx.doi.org/10.21776/ub.jemis.2021.009.02.1.

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There are numerous optimization method to solve the traveling salesman problem, TSP. One of methods is metaheuristics which is the state of the art algorithm that can solve the large and complex problem. In this research, three of well-known nature inspired population based metaheuristics algorithm: Ant Colony Optimization – ACO, Artificial Bee Colony – ABC and Particle Swarm Optimization – PSO are compared to solve the 29 destinations by using Matlab program. The ACO produces the shortest distance, 94 kilometers and is more efficient than ABC and PSO methods.

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Jamaluddin, Siti Hafawati, Noor Ainul Hayati Mohd Naziri, Norwaziah Mahmud, and Nur Syuhada Muhammat Pazil. "Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm." Journal of Computing Research and Innovation 7, no. 2 (2022): 121–31. http://dx.doi.org/10.24191/jcrinn.v7i2.295.

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Travelling Salesman Problem (TSP) is a list of cities that must visit all cities that start and end in the same city to find the minimum cost of time or distance. The Artificial Bee Colony (ABC) algorithm was used in this study to resolve the TSP. ABC algorithms is an optimisation technique that simulates the foraging behaviour of honey bees and has been successfully applied to various practical issues. ABC algorithm has three types of bees that are used by bees, onlooker bees, and scout bees. In Bavaria from the Library of Traveling Salesman Problem, the distance from one city to another has

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Nagy, Zsuzsanna, Ágnes Werner-Stark, and Tibor Dulai. "An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing Problems." Mathematics 10, no. 13 (2022): 2205. http://dx.doi.org/10.3390/math10132205.

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The Capacitated Arc Routing Problem (CARP) is a combinatorial optimization problem, which requires the identification of such route plans on a given graph to a number of vehicles that generates the least total cost. The Dynamic CARP (DCARP) is a variation of the CARP that considers dynamic changes in the problem. The Artificial Bee Colony (ABC) algorithm is an evolutionary optimization algorithm that was proven to be able to provide better performance than many other evolutionary algorithms, but it was not used for the CARP before. For this reason, in this study, an ABC algorithm for the CARP

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Bolaji, Asaju La’aro, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, and Mohammed A. Awadallah. "A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems." Journal of Intelligent Systems 24, no. 1 (2015): 37–54. http://dx.doi.org/10.1515/jisys-2014-0002.

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AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase

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Du, Zhenxin, and Keyin Chen. "Enhanced artificial bee colony with novel search strategy and dynamic parameter." Computer Science and Information Systems 16, no. 3 (2019): 939–57. http://dx.doi.org/10.2298/csis180923034d.

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There is only one guiding solution in the search equation of Gaussian bare-bones artificial bee colony algorithm (ABC-BB), which is easy to result in the problem of premature convergence and trapping into the local minimum. In order to enhance the capability of escaping from local minimum without loss of the exploitation ability of ABC-BB, a new triangle search strategy is proposed. The candidate solution is generated among the triangle area formed by current solution, global best solution and any randomly selected elite solution to avoid the premature convergence problem. Moreover, the probab

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Otay, Irem, Embiye Senturk, and Ferhan Çebi. "An integrated fuzzy approach for classifying slow-moving items." Journal of Enterprise Information Management 31, no. 4 (2018): 595–611. http://dx.doi.org/10.1108/jeim-02-2018-0028.

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Purpose The purpose of this paper is to propose a new integrated method for evaluating inventory of slow-moving items by introducing the application of fuzzy AHP method with interval Type-2 fuzzy sets (IT2FSs) and ABC analysis. Design/methodology/approach In the study, fuzzy analytic hierarchy process (AHP) method with IT2FSs is employed to set the importance of criteria. The weights obtained from IT2 fuzzy AHP are used to classify slow-moving items in ABC analysis. In the application part, a real-life case study is presented. Findings The result of this study indicates that an integrated appr

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Hatefi, S. M., and S. A. Torabi. "A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification." Advances in Decision Sciences 2015 (January 1, 2015): 1–11. http://dx.doi.org/10.1155/2015/645746.

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Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight l

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Amarjeet and Jitender Kumar Chhabra. "TA-ABC: Two-Archive Artificial Bee Colony for Multi-objective Software Module Clustering Problem." Journal of Intelligent Systems 27, no. 4 (2018): 619–41. http://dx.doi.org/10.1515/jisys-2016-0253.

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Abstract Multi-objective software module clustering problem (M-SMCP) aims to automatically produce clustering solutions that optimize multiple conflicting clustering criteria simultaneously. Multi-objective evolutionary algorithms (MOEAs) have been a most appropriate alternate for solving M-SMCPs. Recently, it has been observed that the performance of MOEAs based on Pareto dominance selection technique degrades with multi-objective optimization problem having more than three objective functions. To alleviate this issue for M-SMCPs containing more than three objective functions, we propose a tw

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Ismail, Basma, Mahmoud Abo El Enin, Mariam Osama, et al. "A Heterogeneous Vehicle Routing Problem with Soft Time Windows for 3PL Company’s Deliveries: A Case Study." Journal Européen des Systèmes Automatisés 54, no. 6 (2021): 909–14. http://dx.doi.org/10.18280/jesa.540614.

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Route optimization is tactically important for companies that must fulfill the demands of different customers with fleet of vehicles, considering multiple factors like: the cost of the resources (vehicles) involved and the operating costs of the entire process. As a case study, a third-party logistics service provider, ABC Company, is introduced to implement optimization on. Furthermore, ABC Company’s problem is defined as route optimization and load consolidation problems that will be solved as heterogeneous vehicle routing problem with soft time windows (HVRPSTW). In this paper’s case, Vehic

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Ji, Junzhong, Hongkai Wei, Chunnian Liu, and Baocai Yin. "Artificial Bee Colony Algorithm Merged with Pheromone Communication Mechanism for the 0-1 Multidimensional Knapsack Problem." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/676275.

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Given a set ofnobjects, the objective of the 0-1 multidimensional knapsack problem (MKP_01) is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfyingmknapsack constraints. In this paper, we have proposed a new artificial bee colony (ABC) algorithm for the MKP_01. The new ABC algorithm introduces a novel communication mechanism among bees, which bases on the updating and diffusion of inductive pheromone produced by bees. In a number of experiments and comparisons, our approach obtains better quality solutions in shorter time than the ABC

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Staniec, Iwona, and Maciej Boniecki. "ABC ANALYSIS IN REVERSE LOGISTICS IN THE COMMERCIAL ENTERPRISE." Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie 21, no. 3 (2020): 87–100. http://dx.doi.org/10.5604/01.3001.0014.4511.

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The reverse logistics related to unsold goods in B2B contracts is a current problem for both practitioners and scientists. This work addresses the problem of using ABC analysis in reverse logistics. The analyzes used weekly data from 2014 to 2017 regarding sales and returns of selected assortment groups. The conducted analyzes showed that the dual ABC categorization due to the volume of sales and returns allows the selection of key groups of assortments for a potential enterprise.

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Yu, Zhenao, Peng Duan, Leilei Meng, Yuyan Han, and Fan Ye. "Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm." Mathematical Biosciences and Engineering 20, no. 2 (2022): 2501–29. http://dx.doi.org/10.3934/mbe.2023117.

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<abstract><p>Effective path planning (PP) is the basis of autonomous navigation for mobile robots. Since the PP is an NP-hard problem, intelligent optimization algorithms have become a popular option to solve this problem. As a classic evolutionary algorithm, the artificial bee colony (ABC) algorithm has been applied to solve numerous realistic optimization problems. In this study, we propose an improved artificial bee colony algorithm (IMO-ABC) to deal with the multi-objective PP problem for a mobile robot. Path length and path safety were optimized as two objectives. Considering

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Saeed, Sima, and Aliakbar Niknafs. "Artificial Bee Colony-Fuzzy Q Learning for Reinforcement Fuzzy Control (Truck Backer-Upper Control Problem)." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 01 (2016): 123–36. http://dx.doi.org/10.1142/s0218488516500070.

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A new method for reinforcement fuzzy controllers is presented by this article. The method uses Artificial Bee Colony algorithm based on Q-Value to control reinforcement fuzzy system; the algorithm is called Artificial Bee Colony-Fuzzy Q learning (ABC-FQ). In fuzzy inference system, precondition part of rules is generated by prior knowledge, but ABC-FQ algorithm is responsible to achieve the best combination of actions for the consequence part of the rules. In ABC-FQ algorithm, each combination of actions is considered a food source for consequence part of the rules and the fitness level of thi

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Axt, V. M., and A. Stahl. "A new approach to the exciton-polariton problem in half-space geometry (ABC-problem)." Solid State Communications 77, no. 3 (1991): 189–93. http://dx.doi.org/10.1016/0038-1098(91)90330-x.

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Verma, Balwant Kumar, and Dharmender Kumar. "A review on Artificial Bee Colony algorithm." International Journal of Engineering & Technology 2, no. 3 (2013): 175. http://dx.doi.org/10.14419/ijet.v2i3.1030.

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In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of

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Hardiansyah, Hardiansyah. "A modified ABC algorithm for solving optimal power flow problem." Serbian Journal of Electrical Engineering 17, no. 2 (2020): 199–211. http://dx.doi.org/10.2298/sjee2002199h.

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This paper presents a modified artificial bee colony (MABC) algorithm for solving the optimal power flow (OPF) problem in power system. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The new algorithm is implemented to the OPF problem so as to minimize the total generation cost when considering the equality and inequality constraints. In order to validate of the proposed alg

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Rickard, Y. S., and N. K. Georgieva. "Problem-independent enhancement of PML ABC for the FDTD method." IEEE Transactions on Antennas and Propagation 51, no. 10 (2003): 3002–6. http://dx.doi.org/10.1109/tap.2003.818000.

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Wood, D. F. "ABC of learning and teaching in medicine: Problem based learning." BMJ 326, no. 7384 (2003): 328–30. http://dx.doi.org/10.1136/bmj.326.7384.328.

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Nasiri, Mohammad Mahdi. "A modified ABC algorithm for the stage shop scheduling problem." Applied Soft Computing 28 (March 2015): 81–89. http://dx.doi.org/10.1016/j.asoc.2014.12.001.

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McMorris, F. R., Henry Martyn Mulder, Beth Novick, and Robert C. Powers. "An ABC-Problem for location and consensus functions on graphs." Discrete Applied Mathematics 207 (July 2016): 15–28. http://dx.doi.org/10.1016/j.dam.2015.12.008.

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Ekhtiari, Mostafa, and Shahab Poursafary. "Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm." ISRN Artificial Intelligence 2013 (December 26, 2013): 1–13. http://dx.doi.org/10.1155/2013/795752.

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It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mix

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Noor Azizah Sidek, Salleh Ahmad Bareduan, Azli Nawawi, and Ten Jia Yee. "Development of Guided Artificial Bee Colony (GABC) Heuristic for Permutation Flowshop Scheduling Problem (PFSP)." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 3 (2023): 393–406. http://dx.doi.org/10.37934/araset.33.3.393406.

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The flowshop is the most often used production system in the sector, and several efforts have been made to improve its efficiency. The NEH (Nawaz, Enscore and Ham) heuristics are one of the promising techniques. The range includes using heuristics and metaheuristics. By adopting a modified version of the Artificial Bee Colony (ABC) algorithm, which has the disadvantage of a slow converge speed, this study aims to boost NEH. To find high-quality results with a faster convergence rate, this study developed a strategy to increase the convergence speed of ABC. Because of the significant performanc

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Zhu, Huizhi, Wenxia Xu, Baocheng Yu, Feng Ding, Lei Cheng, and Jian Huang. "A Novel Hybrid Algorithm for the Forward Kinematics Problem of 6 DOF Based on Neural Networks." Sensors 22, no. 14 (2022): 5318. http://dx.doi.org/10.3390/s22145318.

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The closed kinematic structure of Gough–Stewart platforms causes the kinematic control problem, particularly forward kinematics. In the traditional hybrid algorithm (backpropagation neural network and Newton–Raphson), it is difficult for the neural network part to train different datasets, causing training errors. Moreover, the Newton–Raphson method is unable to operate on a singular Jacobian matrix. In this study, in order to solve the forward kinematics problem of Gough–Stewart platforms, a new hybrid algorithm is proposed based on the combination of an artificial bee colony (ABC)–optimized

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Deng, Xiaoyi. "An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce." Journal of Electronic Commerce in Organizations 11, no. 2 (2013): 53–63. http://dx.doi.org/10.4018/jeco.2013040105.

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Customer segmentation can enable company administrators to establish good customer relations and refine their marketing strategies to match customer expectations. To achieve optimal segmentation, a hybrid Artificial Bee Colony algorithm (ABC) is proposed to classify customers in mobile e-commerce environment, which is named KP-ABC. KP-ABC is based on three famous algorithms: the K-means, Particle Swarm Optimization (PSO), and ABC. The author first applied five clustering algorithms to a mobile customer segmentation problem using data collected from a well established chain restaurant which has

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