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A Novel whale optimization algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

MOBILE NETWORKS & APPLICATIONS 2018年第4期23卷 723-733页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim Sangaiah, Arun Kumar Ahmed, Syed Hassan Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept El Zera Sq Zagazig Sharqiyah Egypt Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle-Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

关键词： whale optimization algorithm Knapsack cipher Subset sum problem Public key cryptosystem Merkle-Hellman Cryptosystem Cryptanalysis

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Theoretical rotational stiffness of the flexible base connection based on parametric study via the whale optimization algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第1期88卷 43-52页

作者： Nawar, Mahmoud T. Matar, Ehab B. Maaly, Hassan M. Alaaser, Ahmed G. Hamdy, Osman Prince Sultan Univ Coll Engn Engn Management Dept Riyadh 11586 Saudi Arabia Zagazig Univ Dept Struct Engn Zagazig Egypt Zagazig Higher Inst Engn & Technol Dept Civil Engn Zagazig Egypt

This paper handles the results of an extensive parametric study on the rotational stiffness of the flexible base connection using ABAQUS program. The results of the parametric study show the relation between the applied moment and the relative rotation for 96 different base connections. The configurations of the studied connections considered different numbers, diameters, and spacing of the anchor bolts along with different thicknesses of the base plate to investigate the effect of these parameters on the rotational stiffness behavior. The results of the previous parametric research used through the whale optimization algorithm (WOA) to detect different equation formulation of the moment-rotation (M-0r) equation to detect optimum equation simulates the general nonlinear rotational behavior of the flexible base connection considering all variables used in the parametric study. WOA is a relatively new promising algorithm, which is used in different types of optimization problems. For more verification, the classical genetic algorithm (GA) is used to make a comparison with WOA results. The results show that WOA is capable of getting an optimum equation of the M-0r relation, which can be used to simulate the actual rotational stiffness of the flexible base connections. The rotational stiffness at H/150 can be calculated using WOA (1) method and be used as a design aid for engineering design.

关键词： flexible base connection flexural stiffness genetic algorithm rotational stiffness whale optimization algorithm

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Energy, exergy, economy analysis, and multi-objective optimization of a novel integrated energy system by combining artificial neural network and whale optimization algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第15期46卷 24179-24196页

作者： Bian, Feiyu You, Shi Zhao, Haoran Chen, Changnian Yu, Zeting Lai, Yanhua Shandong Univ Sch Energy & Power Engn Jinan 250061 Peoples R China Tech Univ Denmark Dept Wind & Energy Syst Lyngby Denmark Shandong Univ Sch Elect Engn Jinan Peoples R China

This study proposed a novel solid oxide fuel cell (SOFC)-integrated system fueled by biogas production from anaerobic fermentation of organic municipal solid wastes. The proposed system composed of the organic Rankine cycle (ORC), the anaerobic digester (AD), the SOFC, Kalina cycle (KC), and the parabolic trough solar collector (PTSC) which provided heat for the continuous and stable operation of anaerobic fermentation. Firstly, the mathematical model was established and then the energy, exergy, and economy analysis were evaluated. The results showed that the total exergy efficiency and the cost rate achieved 30.96% and 19.68$/h under design conditions. It was found that the total exergy efficiency increased as the increasing in the evaporating pressure of ORC and the basic ammonia solution concentration, but it decreased with the increase in the solar radiation intensity. When the input temperature and the current density of SOFC were increased, the total exergy efficiency was increased firstly then decreased and reached the maximum value of 439.2 and 481 kW at the SOFC input temperature of 628.6 degrees C and the current density of 8286 A/m(2). Besides, the cost rate was increased with the increase of the power consumption of the main components. The Pareto frontier was obtained by using the non-dominated sorting whale optimization algorithm (NSWOA) which was employed to perform the multi-objective optimization, and the comprehensive decision-making method (TOPSIS) was used to get the optimal solution. The optimal solution showed that the total exergy efficiency and the cost rate could reach 39.56% and 14.23 $/h, respectively. Furthermore, in order to reduce optimization time and improve the accuracy of surrogate model, this study examined an improved artificial neural network (ANN) algorithm combining data-driven surrogate model and whale optimization algorithm (WOA) to replace the physical model. It was also found that the time using the developed surrogate mode

关键词： artificial neural network optimization parameter analysis solar and biogas energy solid oxide fuel cell whale optimization algorithm

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A hybrid whale optimization algorithm with artificial bee colony

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SOFT COMPUTING 2022年第5期26卷 2075-2097页

作者： Tang, Chenjun Sun, Wei Xue, Min Zhang, Xing Tang, Hongwei Wu, Wei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Hunan Prov Key Lab Intelligent Robot Technol Elec Changsha 410082 Hunan Peoples R China

In this paper, the defects and deficiencies of the recently proposed whale optimization algorithm (WOA) are improved. A whale optimization algorithm mixed with an artificial bee colony (ACWOA) is proposed to solve the WOA problems of slow convergence, low precision, and easy to fall into local optimum. The ACWOA algorithm integrates the artificial bee colony algorithm and chaotic mapping, effectively avoiding the local optimal situation and improving the quality of the initial solution. Also, nonlinear convergence factors and adaptive inertia weight coefficients are added to accelerate the convergence rate. To verify the performance of the improved algorithm, 20 benchmark functions and CEC2019 multimodal multi-objective benchmark functions have been used to compare ACWOA with the classical intelligent population algorithms (PSO, MVO, and GWO) and the recent state-of-the-art algorithms (CWOA, HWPSO, and HIWOA) in recent years. The proposed algorithm is applied to two well-known engineering mathematical models and a real application (the quality process control). The experiments show that the ACWOA algorithm has strong competitiveness in convergence speed and solution accuracy and has certain practical value in complex mathematical model scenarios.

关键词： whale optimization algorithm Artificial bee colony algorithm Nonlinear convergence factor Adaptive inertia weight

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A novel binarization technique based on whale optimization algorithm for better restoration of palm leaf manuscript

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年 1-8页

作者： Alexander, T. Jerry Kumar, S. Suresh Sathyabama Inst Sci & Technol Fac Elect Engn Chennai Tamil Nadu India Swarnandhra Coll Engn & Technol Narasapur India

Palm leaf manuscript is a rich source of information pertaining to culture and heritage from the ancient period which is written on Palm leaves. They share surplus knowledge base in the field of art, medicine, culture, science, astronomy, astrology, crafts, literature, etc. Due to environmental effects and various other factors like handling, storage methods, etc. these manuscripts undergoes degradation. Hence for preservation of the information contained in the palm leaves, digitization of the manuscript is needed. The manuscript is either scanned or photographed for digitization and stored for preserving the content. For storage, enormous storage space is required and in addition, the degradation due to the noise should be removed for restoration of the text in the Palm leaf manuscript. Textual retrieval is done by using binarization method. Many binarization techniques such as Local threshold methods, Global thresholding, Otsu histogram thresholding, Adaptive thresholding techniques are available in the literature. Even then lots of challenges are present due to the degradation of the Palm-leaf manuscript. Swarm intelligence techniques are gaining importance in almost all domains for the optimization of parameters. In this present work, an attempt is made to use whale optimization algorithm for optimization of Adaptive thresholding and the results are compared with the existing techniques. It proves the ascendance of the proposed technique over the prevailing techniques.

关键词： Binarization Palm leaf manuscript Niblack Sauvola Adaptive thresholding whale optimization algorithm Text retrieval Image restoration

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A Hybrid Model Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise, GRU Network and whale optimization algorithm for Wind Power Prediction

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IEEE ACCESS 2023年 11卷 62840-62854页

作者： Sheng, Andi Xie, Lewei Zhou, Yixiang Wang, Zhen Liu, Yuechao North China Elect Power Univ Comp Dept Baoding 071000 Hebei Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430000 Hubei Peoples R China Anhui Med Univ Sch Hlth Adm Hefei 230012 Anhui Peoples R China Zhejiang Yuexiu Univ Hotel Management Dept Shaoxing 312000 Zhejiang Peoples R China North China Elect Power Univ Dept Math & Phys Baoding 071003 Hebei Peoples R China

To ensure the safe and stable operation of power systems, accurate prediction of wind power generation is particularly important. However, due to the randomness, fluctuation, and intermittency of wind energy, as well as the challenges in determining the hyperparameters of the gated recurrent unit (GRU) network, this paper proposes an innovative "decomposition-optimization-reconstruction" prediction method. To enhance the accuracy of wind power prediction, a sophisticated wind power hybrid prediction model based on the GRU network, combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the whale optimization algorithm (WOA) has been proposed in this paper. Among the efficient decomposition algorithms are successive variational mode decomposition (SVMD) and CEEMDAN. Taking into account adaptability issues, in the data decomposition stage, the CEEMDAN method is chosen to decompose the wind power time series into different sub-sequence components. This approach allows for a better representation of the fluctuation characteristics of each sub-sequence at various time scales. In the prediction optimization stage, the GRU prediction method is used to predict each sub-sequence component and the WOA algorithm is combined to optimize the hyperparameters of each GRU. This approach demonstrates significant advantages in improving wind power prediction accuracy, enhancing generalization capability, and strengthening adaptability. In the prediction reconstruction stage, the prediction results of each sub-sequence component are superimposed to obtain the final wind power prediction value. Finally, the model is verified using actual wind power data from a power plant in the northwest, and the simulation results show that the wind power prediction model based on CEEMDAN-WOA-GRU has significant advantages in prediction accuracy and stability compared to other models. This model provides strong support for optimizing wind energy grid integration

关键词： Decomposition-optimization-reconstruction gated recurrent unit complete ensemble empirical mode decomposition with adaptive noise whale optimization algorithm wind power prediction

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Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第5期17卷 5987-6025页

作者： Zhou, Yongquan Niu, Yanbiao Luo, Qifang Jiang, Ming Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Guangxi Inst Digital Technol Nanning 530000 Peoples R China

This paper presents an improved teaching learning-based whale optimization algorithm (TSWOA) used the simplex method. First of all, the combination of WOA algorithm and teaching learning-based algorithm not only achieves a better balance between exploration and exploitation of WOA, but also makes whales have self-learning ability from the biological background, and greatly enriches the theory of the original WOA algorithm. Secondly, the WOA algorithm adds the simplex method to optimize the current worst unit, averting the agents to search at the boundary, and increasing the convergence accuracy and speed of the algorithm. To evaluate the performance of the improved algorithm, the TSWOA algorithm is employed to train the multi-layer perceptron (MLP) neural network. It is a difficult thing to propose a well-pleasing and valid algorithm to optimize the multi-layer perceptron neural network. Fifteen different data sets were selected from the UCI machine learning knowledge and the statistical results were compared with GOA, GSO, SSO, FPA, GA and WOA, severally. The statistical results display that better performance of TSWOA compared to WOA and several well-established algorithms for training multi-layer perceptron neural networks.

关键词： teaching learning-based whale optimization algorithm multi-layer perceptron (MLP) neural network metaheuristic algorithm

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Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction

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RENEWABLE ENERGY 2022年 197卷 668-682页

作者： Zhang, Chu Ji, Chunlei Hua, Lei Ma, Huixin Nazir, Muhammad Shahzad Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Wind energy, as clean energy, has attracted more and more attention. Wind power generation is easily threat-ened by the irregular fluctuation of wind speed, which interferes with the safety and stability of power system. In this study, a wind speed interval prediction method based on variational mode decomposition (VMD), phase space reconstruction (PSR), whale optimization algorithm (WOA), quantile regression (QR) and gated recurrent unit (GRU) is proposed. Firstly, the wind speed time series is decomposed into a variety of intrinsic mode functions (IMFs) through VMD to reduce the stochasticity. Secondly, all IMFs are then reconstructed using PSR to get the optimal input variables of the model. Then, the QRGRU model is optimized by the improved WOA to get the optimal QRGRU model parameters. Then, the wind speed interval prediction model of PSR-IWOA-QRGRU is established for each intrinsic mode function. Finally, the prediction results of each component are superimposed to realize the wind speed interval prediction. By checking on three different data sets, the effectiveness of the proposed method in wind speed interval prediction is proved.

关键词： Wind speed forecast Gated recurrent unit Quantile regression Variational mode decomposition whale optimization algorithm

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A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm

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APPLIED INTELLIGENCE 2020年第11期50卷 3808-3821页

作者： Chen, Jiaoliao Zhuo, Xingai Xu, Fang Wang, Jiacai Zhang, Dan Zhang, Libin Zhejiang Univ Technol Minist Educ Key Lab E&M Hangzhou 310012 Zhejiang Peoples R China York Univ Lassonde Sch Engn Dept Mech Engn Toronto ON M2J 4A6 Canada

The least squares support vector machine (LSSVM) is a useful binary classifier, but its performance is limited due to the lack of sparseness. The density-dependent quantized LSSVM (DSM) with quantized input data can increase the sparseness to effectively accomplish binary classification. However, the DSM cannot be directly used in multi-classification applications for most practical data-classification problems. We propose a novel multi-classifier based on a density-dependent quantized binary tree LSSVM (DBSM) and the logistic global whale optimization algorithm (LWA) to improve multi-classification accuracy and computational efficiency. The DBSM consists of multiple DSM classifiers, which hierarchically divide data according to a modified binary tree architecture. The tree architecture is constructed quickly and correctly with the quantized data instead of the original input data. An appropriate initial population of DBSM parameters is generated by using a logistic map and an improved opposition-based learning strategy. Then, the DBSM parameters are optimized by the whale optimization algorithm integrated with the gbest-guided artificial bee colony algorithm. According to the experimental results, the DBSM solves multi-classification problems faster than the one-versus-one based support vector machine (OVO-SVM) and the one-versus-all based LSSVM without sacrificing accuracy. The LWA precisely finds the optimal DBSM parameters without a heavy computational burden, in contrast to recent optimization algorithms. The proposed classifier achieves a 3.39% higher accuracy and consumes 52.83% less time than the genetic algorithm-based OVO-SVM. These results prove that the LWA-DBSM can complete multi-class classification tasks precisely and quickly.

关键词： Multi-class classification Least squares support vector machine whale optimization algorithm Binary tree

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Inversion of the surface duct from radar sea clutter using the improved whale optimization algorithm

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ELECTROMAGNETICS 2019年第8期39卷 611-627页

作者： Yang, Chao Wang, Yizhi Xian Univ Posts & Telecommun Sch Sci Xian Shaanxi Peoples R China

An improved whale optimization algorithm (IWOA) is presented to estimate the surface duct. First, the iteration and selection mechanism in whale optimization algorithm (WOA) are modified to avoid local optimum. Then, orthogonal crossover is embedded into WOA to improve its exploration ability. Finally, the IWOA is applied to benchmark functions and the optimization problem of surface duct to test the performance of IWOA, and the results are compared with those of genetic algorithm (GA), WOA and variant WOA. The comparison results show that IWOA has best accuracy and stability for almost all the benchmark functions and the inversion of the surface duct.

关键词： Surface duct inversion whale optimization algorithm radar sea clutter orthogonal crossover

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