Relevant bibliographies by topics / Artificial Bee Colony Algorithms / Journal articles
To see the other types of publications on this topic, follow the link: Artificial Bee Colony Algorithms.

Journal articles on the topic 'Artificial Bee Colony Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Artificial Bee Colony Algorithms.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Patel, Subhash, and Rajesh A. Thakker. "Parameter Space Exploration for Analog Circuit Design Using Enhanced Bee Colony Algorithm." Journal of Circuits, Systems and Computers 28, no. 09 (August 2019): 1950153. http://dx.doi.org/10.1142/s0218126619501536.

Full text
Abstract:
In this work, novel swarm optimization algorithm based on the Artificial Bee Colony (ABC) algorithm called Enhanced Artificial Bee Colony (EABC) algorithm is proposed for the design and optimization of the analog CMOS circuits. The new search strategies adopted improve overall performance of the proposed algorithm. The performance of EABC algorithm is compared with other competitive algorithms such as ABC, GABC (G-best Artificial Bee Colony Algorithm) and MABC (Modified Artificial Bee Colony Algorithm) by designing three CMOS circuits; Two-stage operational amplifier, low-voltage bulk driven OTA and second generation low-voltage current conveyor in 0.13 [Formula: see text]m and 0.09[Formula: see text][Formula: see text]m CMOS technologies. The obtained results clearly indicate that the performance of EABC algorithm is better than other mentioned algorithms and it can be an effective approach for the automatic design of the analog CMOS circuits.
APA, Harvard, Vancouver, ISO, and other styles
2

Minetti, Gabriela, and Carolina Salto. "Artificial Bee Colony Algorithm Improved with Evolutionary Operators." Journal of Computer Science and Technology 18, no. 02 (October 4, 2018): e13. http://dx.doi.org/10.24215/16666038.18.e13.

Full text
Abstract:
In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.
APA, Harvard, Vancouver, ISO, and other styles
3

Qin, Quande, Shi Cheng, Qingyu Zhang, Li Li, and Yuhui Shi. "Artificial Bee Colony Algorithm with Time-Varying Strategy." Discrete Dynamics in Nature and Society 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/674595.

Full text
Abstract:
Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.
APA, Harvard, Vancouver, ISO, and other styles
4

Verma, Balwant Kumar, and Dharmender Kumar. "A review on Artificial Bee Colony algorithm." International Journal of Engineering & Technology 2, no. 3 (June 21, 2013): 175. http://dx.doi.org/10.14419/ijet.v2i3.1030.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

Balasubramani, Kamalam, and Karnan Marcus. "A Comprehensive review of Artificial Bee Colony Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (June 23, 2013): 15–28. http://dx.doi.org/10.24297/ijct.v5i1.4382.

Full text
Abstract:
The Artificial Bee Colony (ABC) algorithm is a stochastic, population-based evolutionary method proposed by Karaboga in the year 2005. ABC algorithm is simple and very flexible when compared to other swarm based algorithms. This method has become very popular and is widely used, because of its good convergence properties. The intelligent foraging behavior of honeybee swarm has been reproduced in ABC.Numerous ABC algorithms were developed based on foraging behavior of honey bees for solving optimization, unconstrained and constrained problems. This paper attempts to provide a comprehensive survey of research on ABC. A system of comparisons and descriptions is used to designate the importance of ABC algorithm, its enhancement, hybrid approaches and applications.
APA, Harvard, Vancouver, ISO, and other styles
6

Xiao, Ren Bin, and Ying Cong Wang. "Research on Cellular Artificial Bee Colony Algorithm and its Computational Experiments." Applied Mechanics and Materials 284-287 (January 2013): 3168–72. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3168.

Full text
Abstract:
It is the research hotspot for evolutionary algorithms to solve the contradiction between exploration and exploitation. Cellular artificial bee colony (CABC) algorithm is proposed by combining cellular automata with artificial bee colony algorithm from the perspective of the neighborhood in this paper. Each bee in the population structure defined in CABC has a fixed position and can only interact with bees in its neighborhood. The overlap between neighborhoods of different bees may make a bee an employed bee in one neighborhood and an onlooker bee in another neighborhood and vice versa, which increases the diversity of the population. The neighborhood and evolutionary rule help to control the selection pressure effectively, and the improved search mechanism in artificial bee colony algorithm is proposed to enhance the local search ability. The experimental results tested on four benchmark functions show that CABC can further balance the relationship between exploration and exploitation when compared with three ABC-based algorithms.
APA, Harvard, Vancouver, ISO, and other styles
7

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 (October 2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

Zou, Wenping, Yunlong Zhu, Hanning Chen, and Xin Sui. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/459796.

Full text
Abstract:
Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
APA, Harvard, Vancouver, ISO, and other styles
9

Sharma, Tarun Kumar, and Millie Pant. "Differential Operators Embedded Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 2, no. 3 (July 2011): 1–14. http://dx.doi.org/10.4018/jaec.2011070101.

Full text
Abstract:
Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.
APA, Harvard, Vancouver, ISO, and other styles
10

Karaboga, Dervis. "Artificial bee colony algorithm." Scholarpedia 5, no. 3 (2010): 6915. http://dx.doi.org/10.4249/scholarpedia.6915.

Full text
APA, Harvard, Vancouver, ISO, and other styles
11

Shah, Habib, Rozaida Ghazali, Nazri Mohd Nawi, and Mustafa Mat Deris. "G-HABC Algorithm for Training Artificial Neural Networks." International Journal of Applied Metaheuristic Computing 3, no. 3 (July 2012): 1–19. http://dx.doi.org/10.4018/jamc.2012070101.

Full text
Abstract:
Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.
APA, Harvard, Vancouver, ISO, and other styles
12

Zhang, Song, and Sanyang Liu. "A Novel Artificial Bee Colony Algorithm for Function Optimization." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/129271.

Full text
Abstract:
It is known that both exploration and exploitation are important in the search equations of ABC algorithms. How to well balance the two abilities in the search process is still a challenging problem in ABC algorithms. In this paper, we propose a novel artificial bee algorithm named as “NABC,” by incorporating the information of the global best solution into the solution search equation of the onlookers stage to improve the exploitation. At the same time, we improve the search equation of the employed bees to keep the exploration. The experimental results of NABC tested on a set of 11 numerical benchmark functions show good performance and fast convergence in solving function optimization problems, compared with variant ABC, DE, and PSO algorithms. The application of NABC on solving five standard knapsack problems shows its effectiveness and practicability.
APA, Harvard, Vancouver, ISO, and other styles
13

Liu, Wen. "A Multistrategy Optimization Improved Artificial Bee Colony Algorithm." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/129483.

Full text
Abstract:
Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.
APA, Harvard, Vancouver, ISO, and other styles
14

Shah, Habib, Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, and Tutut Herawan. "Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification." International Journal of Applied Evolutionary Computation 4, no. 3 (July 2013): 58–74. http://dx.doi.org/10.4018/jaec.2013070106.

Full text
Abstract:
The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task.
APA, Harvard, Vancouver, ISO, and other styles
15

Chun-Feng, Wang, Liu Kui, and Shen Pei-Ping. "Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/832949.

Full text
Abstract:
Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
APA, Harvard, Vancouver, ISO, and other styles
16

Danish, Zeeshan, Habib Shah, Nasser Tairan, Rozaida Gazali, and Akhtar Badshah. "Global Artificial Bee Colony Search Algorithm for Data Clustering." International Journal of Swarm Intelligence Research 10, no. 2 (April 2019): 48–59. http://dx.doi.org/10.4018/ijsir.2019040104.

Full text
Abstract:
Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.
APA, Harvard, Vancouver, ISO, and other styles
17

Xu, Yunfeng, Ping Fan, and Ling Yuan. "A Simple and Efficient Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/526315.

Full text
Abstract:
Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.
APA, Harvard, Vancouver, ISO, and other styles
18

Soghrati, F., and R. Moeini. "Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions." Journal of Hydroinformatics 22, no. 2 (December 24, 2019): 263–80. http://dx.doi.org/10.2166/hydro.2019.125.

Full text
Abstract:
Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.
APA, Harvard, Vancouver, ISO, and other styles
19

Bansal, Jagdish Chand, Harish Sharma, Atulya Nagar, and K. V. Arya. "Balanced artificial bee colony algorithm." International Journal of Artificial Intelligence and Soft Computing 3, no. 3 (2013): 222. http://dx.doi.org/10.1504/ijaisc.2013.053392.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

Sharma, Tarun Kumar, and Millie Pant. "Shuffled artificial bee colony algorithm." Soft Computing 21, no. 20 (May 11, 2016): 6085–104. http://dx.doi.org/10.1007/s00500-016-2166-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
21

Li, Jun-yi, Yi-ding Zhao, Jian-hua Li, and Xiao-jun Liu. "Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation." Mathematical Problems in Engineering 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/272947.

Full text
Abstract:
This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.
APA, Harvard, Vancouver, ISO, and other styles
22

Alqattan, Zakaria N., and Rosni Abdullah. "A hybrid artificial bee colony algorithm for numerical function optimization." International Journal of Modern Physics C 26, no. 10 (June 24, 2015): 1550109. http://dx.doi.org/10.1142/s0129183115501090.

Full text
Abstract:
Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).
APA, Harvard, Vancouver, ISO, and other styles
23

Panniem, Amnat, and Pikul Puphasuk. "A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems." Journal of Applied Mathematics 2018 (December 18, 2018): 1–9. http://dx.doi.org/10.1155/2018/1237823.

Full text
Abstract:
Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.
APA, Harvard, Vancouver, ISO, and other styles
24

Ghosh, Smarajit, Manvir Kaur, Suman Bhullar, and Vinod Karar. "Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems." Energies 12, no. 3 (February 11, 2019): 551. http://dx.doi.org/10.3390/en12030551.

Full text
Abstract:
The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.
APA, Harvard, Vancouver, ISO, and other styles
25

Huang, Xingwang, Xuewen Zeng, Rui Han, and Xu Wang. "An enhanced hybridized artificial bee colony algorithm for optimization problems." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 87. http://dx.doi.org/10.11591/ijai.v8.i1.pp87-94.

Full text
Abstract:
Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.
APA, Harvard, Vancouver, ISO, and other styles
26

Zou, Wenping, Yunlong Zhu, Hanning Chen, and Beiwei Zhang. "Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2011 (2011): 1–37. http://dx.doi.org/10.1155/2011/569784.

Full text
Abstract:
Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.
APA, Harvard, Vancouver, ISO, and other styles
27

Aslan, Selcuk. "A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm." Computational Intelligence and Neuroscience 2019 (April 1, 2019): 1–24. http://dx.doi.org/10.1155/2019/5012313.

Full text
Abstract:
Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.
APA, Harvard, Vancouver, ISO, and other styles
28

Olukunle Kolawole, Soyinka, and Duan Haibin. "Satellite formation keeping via chaotic artificial bee colony." Aircraft Engineering and Aerospace Technology 89, no. 2 (March 6, 2017): 246–56. http://dx.doi.org/10.1108/aeat-02-2014-0019.

Full text
Abstract:
Purpose Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms. Design/methodology/approach The chaotic artificial bee colony algorithm is a variant of the basic artificial bee colony algorithm. The algorithm mimics the behaviour of bees in their search for food sources. This paper uses the algorithm in optimizing feedback controller parameters for a satellite formation control problem. The problem is formulated to optimize the controller parameters while minimizing a fuel- and state-dependent cost function. The dynamical model of the satellite is based on Gauss variational equations with J2 perturbation. Detailed implementation of the procedure is provided, and experimental results of using the algorithm are also presented to show feasibility of the method. Findings The experimental results indicate the feasibility of this approach, clearly showing the effective control of the transients that arise because of J2 perturbation. Originality/value This paper applied a swarm intelligence approach to the problem of estimating optimal feedback control parameter for a pair of satellites in a formation.
APA, Harvard, Vancouver, ISO, and other styles
29

Alaidi, Abdul Hadi, Chen S. Soong Der, and Yeng Weng Leong. "Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 16 (August 23, 2021): 172. http://dx.doi.org/10.3991/ijim.v15i16.24171.

Full text
Abstract:
The artificial bee colony (ABC) is a well-studied algorithm developed to solve continuous function optimization problems by Karboga and Akay in 2009. ABC has been proven to be more effective than other biological-inspired algorithms with good exploration. However, ABC suffers from low exploitation and slow convergence in some cases. The ABC algorithm study has risen significantly over the past decade, with many researchers trying to improve ABC performance and apply it to solve problems. One method to enhance ABC is to borrow exploration technique from other algorithms. Researchers use pheromone, which is a technique used by Ant Colony optimization algorithm, to enhance ABC and addressed several aspects of using a pheromone to enhance the ABC. This systematic review aims to review and analysis articles about using pheromone to enhance ABC. Articles on related topics were systematically searched in four major databases namely Scopus, Web of Science, Association for Computing Machinery ACM and Google Scholar. To ensure that all research articles were considered the start date is not restrictions the search carry out till February 2021.Five articles were selected based on our inclusion and exclusion criteria for the systematic review. The results show that the use Pheromone to enhance ABC can increase the ABC exploitation ability and overcoming the late convergence. This paper also illustrates several potential pheromone using for future work.
APA, Harvard, Vancouver, ISO, and other styles
30

Vazquez, Roberto A., and Beatriz A. Garro. "Training Spiking Neural Models Using Artificial Bee Colony." Computational Intelligence and Neuroscience 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/947098.

Full text
Abstract:
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.
APA, Harvard, Vancouver, ISO, and other styles
31

Zhang, Shudong, Yaru Shao, and Lijuan Zhou. "Optimized Artificial Bee Colony Algorithm for Web Service Composition Problem." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 327–32. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1056.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

Tajudin, Aimi Idzwan, Ahmad Asri Abd Samat, Pais Saedin, and Mohamad Adha Mohamad Idin. "Application of Artificial Bee Colony Algorithm for Distribution Network Reconfiguration." Applied Mechanics and Materials 785 (August 2015): 38–42. http://dx.doi.org/10.4028/www.scientific.net/amm.785.38.

Full text
Abstract:
—Network reconfiguration is a process of changing the original structure of the distribution network system with the intention of balancing the load in every system’s feeder at the same time to optimize the operation of the system. The process involve the changing of switching state (tie switches and sectionalize switches), with the aim to find the best combination that can increase the performance of the system while satisfying with the operational constraints. The extreme necessity to the process has become a challenging mission for the researcher to overcome the reconfiguration problems. Recent years have seen a rapid development of evolutionary algorithms and swarm intelligence based algorithms to resolve for network reconfiguration problems. For that reason, this report deals with Artificial Bee Colony (ABC) algorithm to be implemented in network reconfiguration procedure to achieve the optimum level of operation. The ease and simplicity of the algorithm and the capability to find the global optimization solution has made this algorithm appropriate for this project. The objective of this work focused on improvements of distribution power system, in terms of minimizing the total real power loss and improving the voltage profile within the acceptable value. The algorithm was tested on two different radial distribution system (33 bus and 69 bus radial distribution systems)
APA, Harvard, Vancouver, ISO, and other styles
33

MA, RUIXIN, XIUJUAN XU, LEI ZHAO, REN CAO, and QIANG FANG. "MUTUAL ARTIFICIAL BEE COLONY ALGORITHM FOR MOLECULAR DOCKING." International Journal of Biomathematics 06, no. 06 (November 2013): 1350038. http://dx.doi.org/10.1142/s1793524513500381.

Full text
Abstract:
Molecular docking method plays an important role on the quest of potential drug candidates, which has been proven to be a valuable tool for virtual screening. Molecular docking is commonly referred to as a parameter optimization problem. During the last decade, some optimization algorithms have been introduced, such as Lamarckian genetic algorithm (LGA) and SODOCK embedded in the AutoDock program. On the basis of the latest docking software AutoDock4.2, we present a novel docking program ABCDock, which incorporates mutual artificial bee colony (MutualABC) into AutoDock. Computer simulation results demonstrate that ABCDock takes precedence over AutoDock and SODOCK, in terms of convergence performance, accuracy, and the lowest energy, especially for highly flexible ligands. It is noteworthy that ABCDock yields a higher success rate. Also, in comparison with the other state-of-the-art docking methods, namely GOLD, DOCK and FlexX, ABCDock provides the smallest RMSD in 27 of 37 cases.
APA, Harvard, Vancouver, ISO, and other styles
34

Afşar, Bekir, and Doğan Aydın. "Comparison of Artificial Bee Colony Algorithms on Engineering Problems." Applied Mathematics & Information Sciences 10, no. 2 (March 1, 2016): 495–505. http://dx.doi.org/10.18576/amis/100211.

Full text
APA, Harvard, Vancouver, ISO, and other styles
35

jie, YU. "Application of Artificial Bee Colony Algorithms in Smart Grid." Journal of Physics: Conference Series 1453 (January 2020): 012083. http://dx.doi.org/10.1088/1742-6596/1453/1/012083.

Full text
APA, Harvard, Vancouver, ISO, and other styles
36

Yang, Jingyuan, Qiaoyong Jiang, Lei Wang, Shuai Liu, Yu-Dong Zhang, Wei Li, and Bin Wang. "An adaptive encoding learning for artificial bee colony algorithms." Journal of Computational Science 30 (January 2019): 11–27. http://dx.doi.org/10.1016/j.jocs.2018.11.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
37

Yavuz, Gürcan, and Doğan Aydin. "Angle Modulated Artificial Bee Colony Algorithms for Feature Selection." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/9569161.

Full text
Abstract:
Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.
APA, Harvard, Vancouver, ISO, and other styles
38

Shah, Habib, Nasser Tairan, Harish Garg, and Rozaida Ghazali. "Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization." Computers 7, no. 4 (December 7, 2018): 69. http://dx.doi.org/10.3390/computers7040069.

Full text
Abstract:
Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing.
APA, Harvard, Vancouver, ISO, and other styles
39

Kantawong, Krittika, and Sakkayaphop Pravesjit. "An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 1 (March 31, 2020): 46–52. http://dx.doi.org/10.37936/ecti-cit.2020141.200016.

Full text
Abstract:
This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.
APA, Harvard, Vancouver, ISO, and other styles
40

Zhao, Yinghao, Quansheng Yan, Zheng Yang, Xiaolin Yu, and Buyu Jia. "A Novel Artificial Bee Colony Algorithm for Structural Damage Detection." Advances in Civil Engineering 2020 (January 17, 2020): 1–21. http://dx.doi.org/10.1155/2020/3743089.

Full text
Abstract:
A novel artificial bee colony (ABC) algorithm to detect structural damage via modal and frequency analyses is proposed (named as TCABC algorithm). Compared to the standard ABC algorithm, tabu search method and chaotic search method are adopted in the proposed algorithm to enhance the exploration and exploitation ability. The tabu search method uses a memory function to avoid the solution being trapped in a local minimum, which increases the exploitation ability. Chaotic search method generates more searching points for finding the global minimum, which increases the exploration ability. Additionally, the first roulette wheel selection is replaced by the tournament selection to enhance the global searching ability of the TCABC algorithm. Several explicit test functions and an implicit damage detection function are employed to check the numerical results obtained from ABC and TCABC algorithms. Afterward, the damage detection accuracy of the TCABC algorithm is verified under different circumstances, and several recommendations are given for using the TCABC algorithm to detect structural damages under actual conditions. Finally, an experimental study is applied to examine the performance of TCABC algorithm for damage detection. The results show the following: (1) compared to traditional ABC algorithm, TCABC algorithm performs better; (2) fewer groups lead to faster convergence as demonstrated by both algorithms used in the same damage situation; (3) TCABC algorithm can infer the locations and extents of the damage when the groupings are inaccurate; (4) the accuracy of the field test data profoundly affects the precision of the damage detection results. In other words, stronger noises result in worse identification results; (5) whether or not the noises exist, the more data are measured, the more accurate the results can be achieved; (6) the TCABC algorithm can efficiently detect structural damage in the experimental study.
APA, Harvard, Vancouver, ISO, and other styles
41

Zhang, Bo Ping, and Guo Qing Li. "A Research of Improved Artificial Bee Colony Algorithm." Applied Mechanics and Materials 303-306 (February 2013): 1373–78. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1373.

Full text
Abstract:
This paper studies an improved artificial bee colony algorithm, and two problems have been solved when the artificial colony algorithm is applied to objective optimization: the problem of slow convergence and premature aging problem. When the improved artificial bee colony algorithm is applied to land resources optimization problems, studies show the following two points. First, compared with the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, artificial bee colony algorithm has better adaptability and robustness in solving multivariate and multi peak global optimization problems. Second, compared with artificial bee colony algorithm, the improved artificial bee colony algorithm converges faster, the overall fitness increases by 8.9%, the maximum error is no more than 1%, and the short and medium term optimization has a high precision.
APA, Harvard, Vancouver, ISO, and other styles
42

Ab. Rashid, M. F. F., N. M. Z. Nik Mohamed, and A. N. Mohd Rose. "A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing." Journal of Mechanical Engineering and Sciences 13, no. 4 (December 30, 2019): 5905–21. http://dx.doi.org/10.15282/jmes.13.4.2019.13.0469.

Full text
Abstract:
Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.
APA, Harvard, Vancouver, ISO, and other styles
43

Jin, Qibing, Nan Lin, and Yuming Zhang. "K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony." Algorithms 14, no. 2 (February 7, 2021): 53. http://dx.doi.org/10.3390/a14020053.

Full text
Abstract:
K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.
APA, Harvard, Vancouver, ISO, and other styles
44

Talatahari, S., H. Mohaggeg, Kh Najafi, and A. Manafzadeh. "Solving Parameter Identification of Nonlinear Problems by Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/479197.

Full text
Abstract:
A new optimization method based on artificial bee colony (ABC) algorithm is presented for solving parameter identification problems. The ABC algorithm as a swarm intelligent optimization algorithm is inspired by honey bee foraging. In this paper, for the first time, the ABC method is developed to determine the optimum parameters of Bouc-Wen hysteretic systems. The proposed method exhibits efficiency, robustness, and insensitivity to noise-corrupted data. The results of the ABC are compared with those other optimization algorithms from the literature to show the efficiency of this technique for solving parameter identification problems.
APA, Harvard, Vancouver, ISO, and other styles
45

Karaboga, Dervis, and Beyza Gorkemli. "Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms." International Journal on Artificial Intelligence Tools 28, no. 01 (February 2019): 1950004. http://dx.doi.org/10.1142/s0218213019500040.

Full text
Abstract:
Artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.
APA, Harvard, Vancouver, ISO, and other styles
46

Gireesha. B, Mr, and . "A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 455. http://dx.doi.org/10.14419/ijet.v7i4.5.20205.

Full text
Abstract:
From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed.
APA, Harvard, Vancouver, ISO, and other styles
47

Zhou, Kai, Yongzhao Wen, Wanying Wu, Zhiyong Ni, Tianguo Jin, and Xiaojun Long. "Cloud Service Optimization Method Based on Dynamic Artificial Ant-Bee Colony Algorithm in Agricultural Equipment Manufacturing." Mathematical Problems in Engineering 2020 (October 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/9134695.

Full text
Abstract:
In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms.
APA, Harvard, Vancouver, ISO, and other styles
48

Guo, Yuquan, Xiongfei Li, Yufei Tang, and Jun Li. "Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/4143638.

Full text
Abstract:
Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC) algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.
APA, Harvard, Vancouver, ISO, and other styles
49

Deng, Guanlong, Hongyong Yang, and Shuning Zhang. "An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7373617.

Full text
Abstract:
This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.
APA, Harvard, Vancouver, ISO, and other styles
50

Dahiya, Brahm Prakash, Shaveta Rani, and Paramjeet Singh. "A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (April 1, 2019): 471–88. http://dx.doi.org/10.33889/ijmems.2019.4.2-039.

Full text
Abstract:
Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the bibliographies on the topic 'Artificial Bee Colony Algorithms' for other source types:
Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography