Laplacian whale optimization algorithm

Singh, Amarjeet

doi:10.1007/s13198-019-00801-0

Cited by 18 publications

(8 citation statements)

References 52 publications

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“…[54] DE's mutation operator search mode parameter search mode is added to switch between exploration and exploitation phases. [55] Laplace's crossover operator Two agents are selected, best one and a random then Laplace's crossover operator is applied to produce two new offspring. [56] Golden sine operator non-linear adaptive weight Golden sine operator is incorporated along with non-linear adaptive weights.…”

Section: Referencementioning

confidence: 99%

“…Bozorgi and Yazdani [54] has used DE's mutation operator to improve WOA exploration and exploitation, then a new parameter called search mode is added to switch between exploration and exploitation phases. After following original WOA procedure in [55], two agents are selected, best one and a random one then Laplace's crossover operator is applied to produce two new offspring. their fitness value is calculated against the worst solution of the current population.…”

Section: Referencementioning

confidence: 99%

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A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

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Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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“…Humpback whale exploits bubble-net process to circle around the prey and hunt it [28,29]. Whales encircle the prey, resembling fish, and might revise their location to locate the realistic end result.…”

Section: Feature Selection Using Enhanced Whale Optimization (Ewo) Al...mentioning

confidence: 99%

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

Balaji¹,

Narayanan²

2022

IJC

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IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.

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“…Furthermore, these values are checked to validate the effectiveness of the algorithm. To judge the effectiveness of the algorithm, it is compared with the latest algorithm such as SSD, 29 ASO, 30 Laplacian whale optimization algorithm (LXWOA), 37 BOA, 31 WOA 38 on different dimensions 10D, 20D, and 30D. It is also compared with well‐known variant of TLBO such as a new variant of TLBO (MTLBO), 39 modified TLBO (newMTLBO), 40 WTLBO, 41 and with basic TLBO on different dimensions 10D, 20D, and 30D.…”

Section: Background and Related Workmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.

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Laplacian whale optimization algorithm

Cited by 18 publications

References 52 publications

A comprehensive survey of whale optimization algorithm: modifications and classification

A comprehensive survey of whale optimization algorithm: modifications and classification

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

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