Artículos de revistas: "ABC-Problem"

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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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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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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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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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11

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 implementation of program code and saves computer RAM. Calculations have shown that the efficiency of the Mur ABC will depend on their thickness. On the one hand, an increase in the thickness of the ABC layers will lead to an increase in the accuracy of calculations, on the other hand, to an increase in the size of the calculation area and, as a result, an increase in RAM. The problem arises of determining the criterion for evaluating the efficiency of ABC to determine their optimal thickness. Goal. Identification of new factors that make it possible to use the Mur ABC as efficiently as the Beranger ABC, while significantly saving computer resources. Result. The expressions for the ABC are presented, taking into account the interaction of all components of the electromagnetic field within a single cell of the FDTD. Calculations of the reflection coefficient – a criterion for evaluating the efficiency of the ABC, are presented. Practical significance. Calculations are presented that allow automating the selection of ABC parameters for their stable operation in solving electrodynamic problems.

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12

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 algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

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13

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 systems and simulations results reveal about the better performance of the proposed Refined ABC algorithm (RABC) algorithm in reducing the real power loss and the voltage profiles within the limits.

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14

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 proposed method works by interleaving the ABC and GA search processes. The GA method updates schedulesby considering the best solution found by the ABC approach. Next the ABC approach picks the solutions found by GAsearch. A new approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. Theperformance of the proposed algorithm is compared against a set of state-of-art algorithms. The simulation results showedthat the proposed algorithm provides an efficient way for solving RCPSP and produce competitive results compared toother algorithms investigated in this work.

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15

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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16

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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17

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 metaheuristic because of the limitation of exact methods. Experimental results, carried on sausage manufacturing in a French food industry, proved the highly efficient performance of the proposed RRT-ABC metaheuristic.

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18

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 factor based on the optimization problem dimension and the local search factor based on the factor library (FL). Furthermore, aimed at preventing the algorithm from falling into local optima, dynamic search balance strategy is proposed and applied to replace the scout bee procedure in ABC. Thus, a hybrid, fast, and enhanced algorithm, HFEABC, is presented. In order to verify its effectiveness, some comprehensive tests among HFEABC and ABC and its variants are conducted on 21 basic benchmark functions and 20 complicated functions from CEC 2017. The experimental results show HFEABC offers better compatibility for different problems than ABC and some of its variants. The HFEABC performance is very competitive.

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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 annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

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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 the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib.

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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 algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.</span>

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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 participants of five elderly members of the Posyandu Lansia who were in accordance with the inclusion and exclusion criteria. The output is the correlation between the score of ABC English with ABC Indonesian and the score of ABC Indonesian with ABC English Back Translation which are tested at different times. All the question score in the ABC English questionnaire correlated with ABC Indonesian, as well as the Indonesian Language ABC correlated with ABC English back translation, have a significant correlation (r> 0.3). The conclusion of this study is that the ABC English has a close meaning with the ABC Indonesian and the ABC Indonesian has a close meaning with the ABC English back translation. The validity and reliability of the Indonesian version of ABC questionnaire is needed to obtain a valid and reliable questionnaire.

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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 participants of five elderly members of the Posyandu Lansia who were in accordance with the inclusion and exclusion criteria. The output is the correlation between the score of ABC English with ABC Indonesian and the score of ABC Indonesian with ABC English Back Translation which are tested at different times. All the question score in the ABC English questionnaire correlated with ABC Indonesian, as well as the Indonesian Language ABC correlated with ABC English back translation, have a significant correlation (r> 0.3). The conclusion of this study is that the ABC English has a close meaning with the ABC Indonesian and the ABC Indonesian has a close meaning with the ABC English back translation. The validity and reliability of the Indonesian version of ABC questionnaire is needed to obtain a valid and reliable questionnaire.

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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 behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.

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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 locations for Istanbul Municipality. Geographical coordinates and healthcare waste amounts of Istanbul hospitals are used to decide the locations of sterilization facilities to be established for reducing the medical waste generated. ABC based clustering is performed for different number of clusters predefined by Istanbul Metropolitan Municipality, and the total cost—the amount of healthcare waste produced by a hospital, multiplied by its distance to the sterilization facility—is calculated to decide the number of facilities to be opened. Benchmark results with four algorithms for test data and with two algorithms for real world problem reveal the superior performance of the proposed methodology.

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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 are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.

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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 been used to find the best solution for the shortest distance. The result shows that the best solution for the shortest distance that travellers have to travel in all the 29 cities in Bavaria is 3974km.

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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 (CARP-ABC) was developed along with a new move operator for the CARP, the sub-route plan operator. The CARP-ABC algorithm was tested both as a CARP and a DCARP solver, then its performance was compared with other existing algorithms. The results showed that it excels in finding a relatively good quality solution in a short amount of time, which makes it a competitive solution. The efficiency of the sub-route plan operator was also tested and the results showed that it is more likely to find better solutions than other operators.

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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 involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

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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 probability of crossover is controlled dynamically according to the successful search experience, which can enable ABC-BB to adapt all kinds of optimization problems with different landscapes. The experimental results on a set of 23 benchmark functions and two classic real-world engineering optimization problems show that the proposed algorithm is significantly better than ABC-BB as well as several recently-developed state-of-the-art evolution algorithms.

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32

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 approach utilizing IT2 fuzzy AHP and ABC analysis can be used as a supportive tool for classification of slow-moving items. The problem is solved under fuzzy environment to handle uncertainties and lack of information about slow-moving items. Practical implications Actual data are provided from an automotive company for prioritizing a various criteria to evaluate and classify stocks and a hypothetical model integrated with IT2 fuzzy AHP and ABC analysis is demonstrated. Originality/value Apart from inventory classification literature, the study integrates fuzzy AHP method by employing interval IT2FSs and ABC analysis to solve the real-life inventory classification problem.

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33

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 linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.

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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 two-archive based artificial bee colony (TA-ABC) algorithm. For this contribution, a two-archive concept has been incorporated in the TA-ABC algorithm. Additionally, an improved indicator-based selection method is used instead of Pareto dominance selection technique. To validate the performance of TA-ABC, an empirical study is conducted with two well-known M-SMCPs, i.e. equal-size cluster approach and maximizing cluster approach, each containing five objective functions. The clustering result produced by TA-ABC is compared with existing genetic based two-archive algorithm (TAA) and non-dominated sorting genetic algorithm II (NSGA-II) over seven un-weighted and 10 weighted practical problems. The comparison results show that the proposed TA-ABC outperforms significantly TAA and NSGA-II in terms of modularization quality, coupling, cohesion, Pareto optimality, inverted generational distance, hypervolume, and spread performance metrics.

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35

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, Vehicles travel from a central depot with a restricted capacity, serving clients just once within a defined time interval and providing a needed demand before returning to the central depot. ABC Company’s problem is mathematically formulated and solved using branch and bound method. The formulation is solved on LINGO. The final output is the route, time, cost, and load of each vehicle.

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36

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 algorithm without the mechanism. We have also compared the solution performance of our approach against some stochastic approaches recently reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches.

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37

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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38

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 the complexity of the multi-objective PP problem, a well-environment model and a path encoding method are designed to make solutions feasible. In addition, a hybrid initialization strategy is applied to generate efficient feasible solutions. Subsequently, path-shortening and path-crossing operators are developed and embedded in the IMO-ABC algorithm. Meanwhile, a variable neighborhood local search strategy and a global search strategy, which could enhance exploitation and exploration, respectively, are proposed. Finally, representative maps including a real environment map are employed for simulation tests. The effectiveness of the proposed strategies is verified through numerous comparisons and statistical analyses. Simulation results show that the proposed IMO-ABC yields better solutions with respect to hypervolume and set coverage metrics for the later decision-maker.</p></abstract>

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39

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 this food source is determined by Q-Value. ABC-FQ Algorithm selects the best food resource, which is the best combination of actions for fuzzy system, using Q criterion. This algorithm tries to generate the best reinforcement fuzzy system to control the agent. ABC-FQ algorithm is used to solve the problem of Truck Backer-Upper Control, a reinforcement fuzzy control. The results have indicated that this method arrives to a result with higher speed and fewer trials in comparison to previous methods.

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40

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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41

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 this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

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42

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 algorithm, it is applied to the standard IEEE 30-bus test system. The results show that the proposed technique provides better solutions than other heuristic techniques reported in literature.

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43

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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44

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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45

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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46

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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47

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 mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem.

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48

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 performance in the makespan value (performance indicator), the Total Greedy was adopted in this study, and the author continued to use it throughout the remainder of the research. This study suggested creating a Guided Artificial Bee Colony (GABC) using the First Job Sequence Arrangement Method and the NEH idea. The investigation was based on Taillard benchmark datasets. According to the findings, ABC frequently gave inconsistent outcomes, but surprisingly, GABC, NEH-based ABC, and ABC consistently produced results that were each 68.75%, 63.33%, and 0.01% better than NEH. Finally, the author can state that this analysis validated ABC's slow convergence problem solutions.

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49

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 BP neural network (ABC–BPNN) and a numerical algorithm. ABC greatly improves the prediction ability of neural networks and can provide a superb initial value to numerical algorithms. In the design of numerical algorithms, a modification of Newton’s method (QMn-M) is introduced to solve the problem that the traditional algorithm model cannot be solved when it is trapped in singular matrix. Results show that the maximal improvement in ABC–BPNN error optimization was 46.3%, while the RMSE index decreased by 42.1%. Experiments showed the feasibility of QMn-M in solving singular matrix data, while the percentage improvement in performance for the average number of iterations and required time was 14.4% and 13.9%, respectively.

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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 operations throughout Japan. The results from the clustering were compared to the existing company customer segmentation data for verifications. Based on the initial analysis, special characteristics from those three algorithms were extracted and modified in our KP-ABC method which performed extremely well with mobile e-commerce applications. The result shows that KP-ABC is at least 2% higher than that of other three algorithms.

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