A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification

Nayak, Janmenjoy; Naik, Bighnaraj

doi:10.1007/s00357-018-9270-1

Cited by 12 publications

(5 citation statements)

References 104 publications

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“…Nayak and Naik [80] presented a hybrid metaheuristic pi-sigma neural network (PSNN) based on honey bee mating that successfully solved the data mining classification problem. eir approach combined HBMO with PSNN and compared it with other techniques such as GA, differential evolution (DE), and PSO.…”

Section: State Of the Art Mbosmentioning

confidence: 99%

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

et al. 2022

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Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem.

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Section: State Of the Art Mbosmentioning

confidence: 99%

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

et al. 2022

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“…The idea of swarm intelligence (SI) which is a branch of artificial intelligence (AI) for machine learning is gaining interest since it combines the heuristic and pheromone measure for various applications. For example, [15] hybrid the honeybee mating optimization (HBMO) behavior with the Pi-Sigma neural network (PSNN) for classification task. An approach by [16] found that by embedding the circle chaotic search of whale optimization algorithm (WOA) in the searching iterations, they were able to effectively select relevant features of breast cancer data.…”

Section: Bio-inspired Algorithms For Imbalanced Learningmentioning

confidence: 99%

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

Razali

Saian

Wah

et al. 2021

IJEECS

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<span>Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellinger-ant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew-insensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC score than ATM.</span>

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“…If the following formulas (6) and 7are established, the start receiving operation receives the partition information from the main process and initializes the subarea flow field.…”

Section: Interactive Visual Communication Network Topology Optimizatimentioning

confidence: 99%

“…erefore, this era not only creates a lot of big data companies but also creates the "big data" hot word, more importantly the generation of big data to promote the vigorous development of big data industry and technology, including visualization technology. In this era of big data, network topology visualization technology has become an important branch of the study of visualization technology; network topology visualization is usually the use of points, lines, graphics, and other elements to form a basic image to represent the mobile network, backbone network, or core network topology and information display [6]. Usually, network topology visualization can clearly and intuitively reflect the current operation of the network to assist network managers for network links, nodes, and other aspects of a comprehensive assessment, prediction, analysis, a clear understanding, and understanding of the network internal specific information, laws, and changes [7].…”

Section: Introductionmentioning

confidence: 99%

Topology Optimization of Interactive Visual Communication Networks Based on the Non-Line-of-Sight Congestion Control Algorithm

Liu

Zhou

2020

Complexity

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In this paper, an in-depth study of interactive visual communication of network topology through non-line-of-sight congestion control algorithms is conducted to address the real-time routing problem of adapting to dynamic topologies, and a delay-constrained stochastic routing algorithm is proposed to enable packets to reach GB within the delay threshold in the absence of end-to-end delay information while improving network throughput and reducing network resource consumption. The algorithm requires each sending node to select an available relay set based on the location of its neighbor nodes and channel state and computes transfer probabilities for each node in the relay set combining the remaining delay of the packet with the distance from the relay node to GB. Based on the obtained transfer probability and local channel state, the sending node passes the packet to the relay node. The convergence of the algorithm is proved and its performance is verified by simulation. The first part of the algorithm is based on the greedy algorithm to deploy and locate the network flying platform nodes with the goal of efficient coverage of the network flying platform nodes, considering the ground base station services. As the delay on each link varies due to the change of channel state, the source and relay nodes asynchronously update the data generation rate and the pairwise parameters based on the received local information and use the obtained optimal values to pass the packets to GB.

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A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification

Cited by 12 publications

References 104 publications

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

Topology Optimization of Interactive Visual Communication Networks Based on the Non-Line-of-Sight Congestion Control Algorithm

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