A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and
            <i>K</i>
            ‐Means

Tran, Dang Cong; Wu, Zhijian; Wang, Zelin; Deng, Changshou

doi:10.1049/cje.2015.10.006

Cited by 31 publications

(21 citation statements)

References 13 publications

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“…When the search equations used by standard ABC algorithm and its mentioned variants are considered, it is clearly seen that one additional term is directly used without applying a multiplication, addition, or subtraction operation. In order to decrease the dependency of the changed parameter to the original value of it, Tran et al modeled the arithmetic crossover method for the candidate generation procedure of the ABC algorithm as in the Equation (12) [12]. In Equation 12,…”

Section: Different Candidate Generation Approaches In Variants Of Abcmentioning

confidence: 99%

“…x k2 are randomly determined food sources by controlling that k1, k2, and i are different source indexes and → x best is the global best food source. For making a discrimination between standard ABC algorithm and the newly proposed ABC algorithm in which the employed and onlooker bees use Equation 12is named as enhanced ABC for short EABC algorithm [12].…”

Section: Different Candidate Generation Approaches In Variants Of Abcmentioning

confidence: 99%

“…Abro and Saleh changed the scout bee phase of the original ABC algorithm by proposing a new scouting characteristics that are based on receiving information of the best food source found so far rather than receiving information of a randomly determined one [11]. Tran et al introduced a new ABC algorithm-based clustering technique called enhanced ABC algorithm/K-means for short EABCK [12]. In EABCK, the search equations used by employed and onlooker bee phases are changed with the additions of the arithmetic mutation operator and the information of the global best solution [12].…”

Section: Introductionmentioning

confidence: 99%

“…Tran et al introduced a new ABC algorithm-based clustering technique called enhanced ABC algorithm/K-means for short EABCK [12]. In EABCK, the search equations used by employed and onlooker bee phases are changed with the additions of the arithmetic mutation operator and the information of the global best solution [12]. Hu et al introduced a novel communication model abbreviated as HCM for ABC algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

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Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

Aslan¹

2019

IJCIS

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Artificial Bee Colony (ABC) algorithm inspired by the intelligent source search and consumption characteristics of the real honey bees is one of the most powerful optimization techniques. Although the existing behaviours of the honey bees in standard ABC algorithm are capable of producing optimal or near optimal solutions for the vast majority of the problems, there are still some intelligent characteristics that are not modeled yet such as decision-making mechanism used by employed bees to determine when the dancing will be completed. In this study, a mechanism that adjusts the dancing durations of the employed bees according to the fitness values of the associated food sources is proposed and integrated to the standard ABC algorithm and its well-known variants. Experimental studies on a set of complex continuous numerical problems showed that the newly proposed dance scheduling approach significantly improved the search and consumption characteristics of the artificial bees of the standard ABC algorithm and its some variants.

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Section: Different Candidate Generation Approaches In Variants Of Abcmentioning

confidence: 99%

Section: Different Candidate Generation Approaches In Variants Of Abcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

Aslan¹

2019

IJCIS

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“…PSO is a meta-heuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions [30,31]. Compared with the other evolutionary algorithm such as artificial fish swarm algorithm [32], genetic algorithm [33,34], artificial bee colony algorithm [35,36], and simulated annealing algorithm [37], the most important advantages of PSO are the few parameters needed to adjust and the easy implementation. IBPSO follows the action of chromosomes in genetic algorithm (GA) and sparsely calculates the eigenvectors of the input AHs.…”

mentioning

confidence: 99%

Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

Sun

Wei

et al. 2019

Photonics

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A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.Photonics 2019, 6, 111 2 of 17 network resources. This phenomenon improves the transparency of an optical system but increases the complexity of network management [1]. The capacity of optical networks has been growing steadily, and the network architectures are becoming more dynamic, complex, and transparent. Optical signals in high-speed optical fiber networks are vulnerable to various transmission impairments, which can change dynamically with time. Therefore, an appropriate monitoring mechanism must be established throughout the optical network to provide accurate and real-time information on the quality of transmission links and the health of optical signals. optical performance monitoring (OPM) technology detects the performance parameters of transmission links or optical network nodes in optical networks, and understands the network status and signal transmission status in real time, and therefore processes and ensures the normal network operation and signal transmission. It is of great significance to guarantee the performance of signal transmission and optimize the allocation and management of network resources. Therefore, in order to realize the next generation dynamic reconfigurable optical network, it is necessary to monitor the important parameters, isolate faults in time, and optimize the processing to ensure the transmission quality of the network [2,3].The optical signal-to-noise ratio (OSNR) is one of the key parameters of OPM. It can realize fault management of an optical ...

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Analysis of Search Space in the Domain of Swarm Intelligence

Patel

Rawat

Patel

2021

Algorithms for Intelligent Systems

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A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K ‐Means

Cited by 31 publications

References 13 publications

Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

Analysis of Search Space in the Domain of Swarm Intelligence

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