Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Nguyen, Binh Minh; Tran, Trung; Nguyen, Thieu Huy; Nguyen, Giang

doi:10.1109/access.2020.2988717

Cited by 27 publications

(12 citation statements)

References 66 publications

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“…These elements are converted to 128 bits. For the above example, the optimal elements in the optimal selected face are «24, 76,80,33,20,62,6,1,45,15,5,27,47,79,40, 77», and the generated binary random key is «000110000100110001010000001 000010001010000111110000001100 000000100101101000011110000010 1000110110010111101001111001010 0001001101».…”

Section: Resultsmentioning

confidence: 99%

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Implementation of modified GSO based magic cube keys generation in cryptography

Mazher¹,

Waleed

2021

EEJET

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Over the last few decades, tremendous and exponential expansion in digital contents together with their applications has emerged. The Internet represents the essential leading factor for this expansion, which provides low-cost communication tools worldwide. However, the main drawback of the Internet is related to security problems. In order to provide secure communication, enormous efforts have been spent in the cryptographic field. Recently, cryptographic algorithms have become essential for increasing information safety. However, these algorithms require random keys and can be regarded as compromised when the random keys are cracked via the attackers. Therefore, it is substantial that the generation of keys should be random and hard to crack. In this paper, this is guaranteed via one of the most efficient nature-inspired algorithms emerged by inspiring the movements of stars, galaxies, and galaxy superclusters in the cosmos that can be utilized with a mathematical model (magic cube) for generating hardly cracking random number keys. In the proposed cryptographic system, the Modified Galactic Swarm Optimization (GSO) algorithm has been utilized in which every row and column of magic cube faces are randomly rotated until reaching the optimal face, and the optimal random elements are selected as optimal key from the optimal face. The generated optimized magic cube keys are used with several versions of RC6 algorithms to encrypt various secret texts. Furthermore, these generated keys are also used for encrypting images using the logical XOR operation. The obtained results of NIST tests proved that the generated keys are random and uncorrelated. Moreover, the security of the proposed cryptographic system was proved

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Section: Resultsmentioning

confidence: 99%

“…GSO works on taking the advantages of particle swarm optimization (PSO) with the use of flexible multiple cycles of exploration and exploitation levels for finding optimal solutions. It can be applied in various problems of real life owing to its efficiency and simple implementation [15].…”

Section: Modified Gso Algorithm Based Magic Cube Key Generationmentioning

confidence: 99%

Implementation of modified GSO based magic cube keys generation in cryptography

Mazher¹,

Waleed

2021

EEJET

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“…For finding the best features, first we update the position by using galactic swarm optimization (GSO) algorithm [29]. GSO position is updated in sea lion optimization to get the optimal feature.…”

Section: Finding Best Featurementioning

confidence: 99%

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

2020

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One of the major and fundamental issue is emotion recognition during the development of an interactive computer system [1-3]. Recognition of facial emotion/expression is essential, because nowadays it place its wide applications in various sectors like psychological distress and pain detection [4]. Some fields like psychology, sociology, and automatic expression recognition, therefore provided a considerable importance for this emotion recognition process to create a highly user affable software and user agents in these fields. This process of automatic facial expression recognition (FER) has exhibited its large implications in the human computer interaction (HCI) field [5]. Recently, the affective computing is considered as the most significant study field in HCI, which highly intends to improve the human-machine interaction by clearly recognizing the emotion Abstract Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

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“…In the process of searching, Levy flight have a great leap and the direction of motion also change dramatically, which makes the algorithm jump out of the local optimum. Therefore, many experts and scholars introduce Levy flight into the natural heuristic algorithm to improve the algorithm's global optimization ability (Haklı and U guz, 2014;Aydo gdu et al,2016;Heidari and Pahlavani, 2017;Ali et al, 2018;Abdulwahab et al, 2019;Kamaruzaman et al, 2013;Zhang et al, 2017;Yongquan et al, 2018;Nguyen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An improved sparrow search algorithm based on levy flight and opposition-based learning

Chen

Zhao

Huang

et al. 2021

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Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

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Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Cited by 27 publications

References 66 publications

Implementation of modified GSO based magic cube keys generation in cryptography

Implementation of modified GSO based magic cube keys generation in cryptography

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

An improved sparrow search algorithm based on levy flight and opposition-based learning

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