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A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing

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ENGINEERING optimization 2022年第12期54卷 1999-2016页

作者： Rana, Nadim Abd Latiff, Muhammad Shafie Abdulhamid, Shafi'i Muhammad Misra, Sanjay Jazan Univ Dept Informat Technol & Secur Jazan Saudi Arabia Univ Teknol Malaysia Fac Engn Sch Comp Johor Baharu Malaysia Fed Univ Technol Dept Cyber Secur Sci Minna Nigeria Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway

Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters;therefore, it is classed as an NP-hard optimization problem. Swarm-based metaheuristics, such as the whale optimization algorithm (WOA), have gained a notable reputation for solving optimization problems. The unique bubble-net hunting behaviour and fast convergence of the algorithm led to the development of a hybrid multi-objective whale optimization algorithm-based differential evolution (M-WODE) technique to solve the VM scheduling problem. The differential evolution (DE) strategy is used to replace the randomly generated solution produced by the WOA to ensure diversity in the solution and to strengthen the local search of the M-WODE. In addition, the DE technique is applied to the Pareto front produced by the WOA to escape local optima entrapment problems. The experimental results showed that the proposed M-WODE outperformed previous algorithms in most cases on makespan and the cost trade-off.

关键词： Virtual machine scheduling metaheuristic algorithm whale optimization algorithm differential evolution cloud computing

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Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 1501-1517页

作者： Li, Maodong Xu, Guanghui Fu, Yuanwang Zhang, Tingwei Du, Li Hubei Univ Technol Dept Automat Wuhan Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan Peoples R China

In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm's convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm's global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems.

关键词： whale optimization algorithm adaptive inertia weight convergence speed variable spiral position update cooperative convergence mechanism

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Optimal dispatching of regional interconnection multi-microgrids based on multi-strategy improved whale optimization algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第6期17卷 766-779页

作者： Wang, Zichen Dou, Zhenhai Dong, Jun Si, Shuqian Wang, Chen Liu, Lianxin Shandong Univ Technol Sch Elect & Elect Engn Qingdao Peoples R China State Grid Liaocheng Power Supply Co Mkt Dept Liaocheng 252000 Shandong Peoples R China

The interconnection of multiple microgrids can effectively improve the operating efficiency of the system and reduce the cost of power generation. First, establish an economic environment optimal dispatch model based on the regional interconnected multi-microgrids system, and formulate a dispatch strategy based on the time-of-use electricity price and the supply-demand relationship between microgrids;Secondly, in order to solve this constrained optimization problem, a whale algorithm improved with five strategies including adaptive inertial weights, dynamic spiral search and generalized opposition-based learning is proposed to obtain higher solution accuracy and anti-premature convergence ability;Finally, combined 12 test functions to test the effectiveness of the improved algorithm, and use the improved algorithm to solve the example model containing two microgrids, and analyze it in two states of interconnection operation and non-interconnection operation. The simulation results show that the regional interconnection operation of the microgrid under the established dispatching strategy can effectively reduce the overall operating cost of the system, and at the same time show the effectiveness of the improved algorithm to solve the optimal dispatching problem. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： multi-microgrids interconnected operation optimal dispatch whale optimization algorithm generalized reverse learning

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Cosine adapted modified whale optimization algorithm for control of switched reluctance motor

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COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 978-1017页

作者： Saha, Nutan Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

whale optimization algorithm (WOA) imitates social conduct of humpback whales which is inspired by bubble net hunting strategy of humpback whales. In the present study, Cosine adapted modified whale optimization algorithm (CamWOA) which is a modified version of WOA, has been proposed where cosine function is incorporated for the selection of control parameter "d" which governs the position of whales during optimization process. Also, correction factors are employed to modify the movement of search agents during the search process. These changes provide a proper balance between exploration and exploitation phases in CamWOA technique. The performance of CamWOA is analyzed by testing on a set of benchmark functions and compared with other state-of-the-art algorithms. It is observed that CamWOA outperforms other state-of-the-art metaheuristic algorithms in majority of benchmark functions. The efficiency of CamWOA is also evaluated by solving a multiobjective engineering problem pertaining to control of switched reluctance motor. The simulation results confirm that CamWOA yields very promising and competitive results compared to that of WOA and other metaheuristic optimization algorithms.

关键词： cosine adapted modified whale optimization algorithm proportional integral controller switch reluctance motor torque ripple whale optimization algorithm

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HPCP-QCWOA: High Performance Clustering Protocol based on Quantum Clone whale optimization algorithm in Integrated Energy System

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 315-332页

作者： Liu, Yang Li, Chaoqun Zhang, Yao Xu, Mengying Xiao, Jing Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi 832000 Peoples R China Univ Cordilleras Baguio 2600 Philippines Beijing Univ Posts & Telecommun Beijing Peoples R China Xinjiang Tianfu Informat Technol Co Ltd E Shihezi 832000 Peoples R China

Combined with highly developed technologies such as cloud computing and the Internet of Things, the integrated energy system (IES) has realized the coordinated scheduling and utilization of various energy resources with its superiority of comprehensive control and high applicability. Meanwhile, approaches to enhance the quality of service (QoS), e.g. network lifetime, energy distribution, and data transmission delay, of an indispensable component of IES, i.e., integrated energy system wireless sensor networks (IESWSNs), have become an urgent research issue. To tackle the issue, a high-performance clustering protocol termed quantum clone whale optimization algorithm (HPCP-QCWOA) is proposed for IESWSNs. By designing a new quantum operator and a new layering clone operator, HPCP-QCWOA can find an approximate optimal clustering scheme to improve the QoS performance of IESWSNs. Unlike other population-based clustering protocols, a new objective function that embraces remaining energy, intra-cluster distance, BS distance, and data transmission delay is designed in the HPCP-QCWOA, which renders its clustering performance more desirable than some other state-of-the-art clustering protocols, i.e., O-LEACH, LDIWPSO, and ARSH-FATI-CHS. Empirically, simulation experiments under different scenarios illustrated that the lifetime of the HPCP-QCWOA-based clustering has increased by 28.78%, 25.50% and 11.22% respectively compared with O-LEACH, LDIWPSO, and ARSH-FATI-CHS. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Integrated energy system Wireless sensor network Clustering Quality of service whale optimization algorithm Quantum optimization

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The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

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COGNITIVE SYSTEMS RESEARCH 2022年 75卷 1-15页

作者： Karasu, Seckin Sarac, Zehra Zonguldak Bulent Ecevit Univ Elect & Elect Engn Dept TR-67100 Zonguldak Turkey Istanbul Medeniyet Univ Elect & Elect Engn Dept TR-34700 Istanbul Turkey

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. whale optimization algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classifi-cation methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbanc

关键词： Power quality disturbances 2D discrete wavelet transform Wavelet families whale optimization algorithm 2D signal processing Evolutionary feature selection

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ESSAWOA: Enhanced whale optimization algorithm integrated with Salp Swarm algorithm for global optimization

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 1期38卷 797-814页

作者： Fan, Qian Chen, Zhenjian Zhang, Wei Fang, Xuhua Fuzhou Univ Coll Civil Engn Fuzhou 350116 Peoples R China Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China Fujian Acad Bldg Res Fuzhou 350025 Peoples R China

In this paper, a novel hybrid meta-heuristic algorithm called ESSAWOA is proposed for solving global optimization problems. The main idea of ESSAWOA is to enhance whale optimization algorithm (WOA) by combining the mechanism of Salp Swarm algorithm (SSA) and Lens Opposition-based Learning strategy (LOBL). The hybridization process includes three parts: First, the leader mechanism with strong exploitation of SSA is applied to update the population position before the basic WOA operation. Second, the nonlinear parameter related to the convergence property in SSA is introduced to the two phases of encircling prey and bubble-net attacking in WOA. Third, LOBL strategy is used to increase the population diversity of the proposed optimizer. The hybrid design is expected to significantly enhance the exploitation and exploration capacity of the proposed algorithm. To investigate the effectiveness of ESSAWOA, twenty-three benchmark functions of different dimensions and three classical engineering design problems are performed. Furthermore, SSA, WOA and seven other well-known meta-heuristic algorithms are employed to compare with the proposed optimizer. Our results reveal that ESSAWOA can effectively and quickly obtain the promising solution of these optimization problems in the search space. The performance of ESSAWOA is significantly superior to the basic WOA, SSA and other meta-heuristic algorithms.

关键词： Salp Swarm algorithm whale optimization algorithm Nonlinear parameter Lens Opposition-based Learning Hybridization

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Parameter Identification of Jiles-Atherton Model Based on Levy whale optimization algorithm

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IEEE ACCESS 2022年 10卷 66711-66721页

作者： Chen, Zhigang Yu, Yue Wang, Yanxue Beijing Univ Civil Engn & Architecture Dept Mech & Elect & Vehicle Engn Beijing 100044 Peoples R China Beijing Engn Res Ctr Monitoring Construct Safety Beijing 100044 Peoples R China

The Jiles-Atherton model is key to researching the hysteresis loop. The focus of scholars across various countries has always been the parameter identification of the Jiles-Atherton model. This paper on the Levy whale optimization algorithm (LWOA), based on the whale optimization algorithm (WOA), proposes to overcome the disadvantage that WOA tends to involve the local optimum. The recommended algorithm uses the Levy flight strategy instead of the encircling prey policy since the former improves the global search. Therefore, the new algorithm is better at stability and calculation accuracy. To substantiate the efficacy of the proposed algorithm, it is tested against six benchmark functions and compared with the WOA, particle swarm optimization (PSO), grey wolf algorithm (GWO), and shuffled frog leaping algorithm (SFLA). In addition, the proposed algorithm is applied to realize two classical engineering problems, such as the tension/compression spring and welded beam design issues. The experimental findings reveal that the proposed algorithm is highly competitive with metaheuristic optimizers and improves the algorithm's performance. To address the poor stability of the J-A model parameter identification, an improved calculation method for parameter k and the reduced parameter ranges of the model parameters a and alpha were combined with LWOA. The proposed algorithm is called C-LWOA, which is compared with LWOA, PSO, GWO, SFLA, and the cuckoo search (CS) based on the data reported in the literature. Moreover, the simulation results demonstrate that the stability and calculation accuracy of the parameter identification by the C-LWOA was significantly strengthened. Equally important, the calculation error was within 0.2%. Finally, the proposed algorithm was subsequently used to fit the actual measurements of the hysteresis loop of permalloy.

关键词： Jiles-Atherton model hysteresis loop levy flight whale optimization algorithm

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An enhanced whale optimization algorithm for DNA storage encoding

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第12期19卷 14142-14172页

作者： Wang, Sijie Zhou, Shihua Yan, Weiqi Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China Auckland Univ Technol Sch Engn Comp & Math Sci Auckland 1010 New Zealand

Metaheuristic algorithms have the drawback that local optimal solutions are prone to precocious convergence. In order to overcome the disadvantages of the whale optimization algorithm, we propose an improved selective opposition whale optimization algorithm (ISOWOA) in this paper. Firstly, the enhanced quasi-opposition learning (EQOBL) is applied to selectively update the position of the predator, calculate the fitness of the population before and after, and retain optimal individuals as the food source position;Secondly, an improved time-varying update strategy for inertia weight predator position is proposed, and the position update of the food source is completed by this strategy. The performance of the algorithm is analyzed by 23 benchmark functions of CEC 2005 and 15 benchmark functions of CEC 2015 in various dimensions. The superior results are further shown by Wilcoxon's rank sum test and Friedman's nonparametric rank test. Finally, its applicability is demonstrated through applications to the field of biological computing. In this paper, our aim is to achieve access to DNA files and designs high-quantity DNA code sets by ISOWOA. The experimental results show that the lower bounds of the multi-constraint storage coding sets implemented in this paper equals or surpasses that of previous optimal constructions. The data show that the amount of the DNA storage cods filtered by ISOWOA increased 2-18%, which demonstrates the algorithm's reliability in practical optimization tasks.

关键词： whale optimization algorithm enhanced quasi-opposition learning time-varying inertia weight DNA storage set

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Vector quantization using whale optimization algorithm for digital image compression

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 20077-20103页

作者： Rahebi, Javad Istanbul Ayvansaray Univ Dept Software Engn Istanbul Turkey

Today, much of the information is storing in images. To transfer information in the form of images, image compression is required. Compressing images reduces the size of images and sends them faster over the network. One of the most methods of image compression is the vector quantization. For vector quantization compression, the codebook is using in cryptography and decryption. The vector quantization compression method typically uses codebooks that are not optimized, which reduces the compression quality of the images. Choosing the optimal codebook makes compression of images with higher quality. Choosing the optimal codebook is a difficult optimization problem and therefore requires intelligent algorithms to solve it. In this paper, the whale optimization algorithm is used to find the optimal codebook in image compression. whale optimization algorithm has different search strategies and is an ideal algorithm for finding the optimal codebook in images compression. Implementation of the proposed algorithm for compression on several standard images shows that the proposed method compresses images with appropriate quality. The proposed method performs more efficient compression than the proposed algorithms such as particle swarm optimization, bat, and firefly algorithms. The signal-to-noise ratio of the proposed method is higher than the compared methods. Experiments on a set of standard images show the proposed method compared to the Fire Fly, Bat, and Differential evolution, Improved Particle Swarm optimization, and Improved Differential Evolution methods with a compression execution time of 60.48% and 10.21, respectively. %, 4.79%, 5.09% and 3.94% decreased. The proposed method in compression has a higher PSNR index of about 17% than the Linde-Buzo-Gray method.

关键词： Image compression Image processing Vector quantization whale optimization algorithm

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