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Hybrid whale optimization algorithm based on symbiosis strategy for global optimization

APPLIED INTELLIGENCE 2023年第13期53卷 16663-16705页

作者： Li, Maodong Xu, Guang-hui Zeng, Liang Lai, Qiang Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China East China Jiaotong Univ Sch Elect & Automation Engn Nanchang 330013 Peoples R China

The whale optimization algorithm (WOA) is a simple structured and easily implemented swarm-based algorithm inspired by the unique bubble-net feeding method of humpback whales. Past studies have shown that WOA performs well in a number of optimization problems. However, it is difficult for WOA to completely free itself from the problems of insufficient convergence accuracy and premature convergence when solving global optimization problems. To address these issues, a hybrid whale optimization algorithm based on symbiotic strategy (HWOAMS) is proposed in this paper. The main idea of the proposed method is to combine the improved symbiotic organisms search algorithm (SOS) with the whale optimization algorithm thus enhancing the search ability of WOA. First, an improved symbiotic phase based on Levy flight and chaos strategy is introduced into the exploration process to enhance the global search capability;Second, an improved mutualism phase based on Brownian motion is used instead of the original shrinking encircling phase to achieve better local exploitation. Third, an improved parasitic phase based on a modified global optimal spiral operator strategy is embedded in the spiral updating position phase to help the algorithm further improve the exploitation efficiency and convergence accuracy. Finally, a global search strategy is proposed to help the algorithm better balance exploration and exploitation. To establish the effectiveness of the new algorithm, extensive simulation experiments are conducted on HWOAMS using the classical function test set, the CEC 2019 function set and four classical engineering problems. Numerical evaluation results indicate that HWOAMS outperforms 18 other algorithms in terms of local optimum avoidance ability and convergence accuracy in a majority of cases, and has better search performance.

关键词： whale optimization algorithm Symbiotic organisms search algorithm Diversity Global optimization problem

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Joint Compressed Sensing and Enhanced whale optimization algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

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IEEE ACCESS 2019年 7卷 95779-95796页

作者： Jiang, Rongkun Wang, Xuetian Cao, Shan Zhao, Jiafei Li, Xiaoran Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Univ Dept Commun & Informat Engn Shanghai 200444 Peoples R China

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

关键词： Compressed sensing OFDM whale optimization algorithm underwater acoustic communication channel estimation pilot allocation

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Sunflower whale optimization algorithm for Resource Allocation Strategy in Cloud Computing Platform

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期116卷 3061-3080页

作者： Subhash, Ligade Sunil Udayakumar, R. Bharath Univ Bharath Inst Higher Educ & Res Dept Comp Sci & Engn Chennai Tamil Nadu India

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower whale optimization algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

关键词： Virtualization Cloud computing Sunflower optimization whale optimization algorithm Cloud services

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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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Enhancing whale optimization algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 651-675页

作者： Li, Jiang Guo, Lihong Li, Yan Liu, Chang Wang, Lijuan Hu, Hui Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Jiangsu Univ Sci & Technol Sch Comp Zhenjiang 212003 Jiangsu Peoples R China Huizhou Univ Dept Iformat Sci & Technol Huizhou 516007 Guangdong Peoples R China

The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz-Ensco-Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： whale optimization algorithm Chaotic maps Flow shop scheduling Makespan Local search Cross selection

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Fuzzy Automatic Disturbance Rejection Control of Quadrotor UAV Based on Improved whale optimization algorithm

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IEEE ACCESS 2023年 11卷 69117-69130页

作者： Shan, Zebiao Wang, Yuhang Liu, Xiaosong Wei, Changbin Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China Changchun Meteorol Instrument Res Inst Changchun 130102 Peoples R China

With the aim of improving the rapidity and accuracy of quadrotor UAV positional trajectory tracking, this paper proposes a fuzzy automatic disturbance rejection control of quadrotor UAV based on improved whale optimization algorithm (IWOA-Fuzzy-ADRC). (1) To address the problems of low accuracy and slow convergence of the traditional whale algorithm, a combination of logistic chaos mapping and skew tent mapping is applied to improve the diversity of the initial population. (2) The global search capability is strengthened to avoid falling into local optimum through introducing the cross operator and Gaussian variational operators. (3) The IWOA is employed to iteratively optimize the adjustment coefficients of the fuzzy active disturbance rejection controller, the calculus gain of the nonlinear state error feedback control (NLSEF), and the error correction coefficients of the expansive state observer (ESO). The simulation results demonstrate that compared with the traditional ADRC controller and PID controller, the proposed IWOA-Fuzzy-ADRC controller can effectively improve the control performance of the system, accurately track the desired flight trajectory, accelerate the dynamic response of the control system, reduce the steady-state error, and enhance the anti-interference capability.

关键词： Quadrotors Control systems Heuristic algorithms Observers Autonomous aerial vehicles whale optimization algorithms Rotors Fuzzy systems Chaotic communication INDEX TERMS Gaussian processes UAV trajectory tracking fuzzy ADRC whale optimization algorithm chaotic mapping cross-operator Gaussian variant operators

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Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 127卷 58-67页

作者： Li, Ling-Ling Sun, Jin Tseng, Ming-Lang Li, Zhi-Gang Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Elect Engn Key Lab Electromagnet Field & Elect Apparatus Rel Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Suwon South Korea China Med Univ Dept Med Res Taichung Taiwan

This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Insulated gate bipolar transistor module Aging degree evaluation Extreme learning machine whale optimization algorithm

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A novel product shape design method integrating Kansei engineering and whale optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Zhao, Xiang Sharudin, Sharul Azim Lv, Han-Lu City Univ Malaysia Kuala Lumpur 46100 Malaysia West Univ Appl Sci Tengchong 679100 Peoples R China Daejin Univ Pochon 11159 South Korea

The focus of consumer desire transitions from product functionality to emotional resonance in experience economy era, wherein emotional needs of users increasingly become a critical factor in product design. However, traditional approaches to product shape design often rely heavily on the designer's intuition and experience, sometimes neglecting to incorporate emotional and humanistic elements into the product's shape, thus resulting in inconsistencies in design results and quality. To address this challenge, this study introduces a novel method for emotionally driven product shape design that integrates Kansei engineering and the whale optimization algorithm (WOA). This approach aims to fulfill consumer emotional demands related to product form and enhance overall user satisfaction. Firstly, the process utilized Python web crawlers to collect online product review texts and product images from e-commerce platforms. Next, Latent Dirichlet Allocation (LDA) and Analytic Hierarchy Process (AHP) were employed to extract representative emotional vocabularies, while representative samples were defined and deconstructed through clustering and morphological analysis. Then, semantic Differential (SD) questionnaires were distributed to collect consumer evaluations of product shape imagery, leading to the development of a user emotional prediction model for product shape. Then, WOA was introduced to optimize the performance of Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. Finally, Particle Swarm optimization (PSO) and Seagull optimization algorithm (SOA) were employed to improve the prediction model, and the effect of these models was compared by the error method. This analysis explored the accuracy of these non-linear models in order to identify the optimal model for application in product form design cases. The scientific validity and effectiveness of this method were demonstrated utilizing whiskey bottle shape design as a case study. The

关键词： Product Shape Design Kansei Engineering Latent Dirichlet Allocation whale optimization algorithm Whiskey Bottle Shape

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Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 107卷 29-42页

作者： Zhang, Xin Liu, Zhiwen Miao, Qiang Wang, Lei Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Sichuan Peoples R China

Condition monitoring and fault diagnosis of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the fault features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing fault features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time frequency atom dictionary is proposed in this paper. Firstly, a combined time-frequency atom dictionary whose atom is a combination of Fourier dictionary atom and impact time-frequency dictionary atom is designed according to the properties of bearing fault vibration signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the atom parameters for best approximating the original signal with the dictionary atoms. The proposed method is validated through analyzing the bearing fault simulation signal and the real vibration signals collected from an experimental bearing and a wheelset bearing of highspeed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Bearing fault diagnosis Sparse decomposition Orthogonal matching pursuit Combined time-frequency atom dictionary whale optimization algorithm

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A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

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COMPUTATIONAL INTELLIGENCE 2021年第1期37卷 176-209页

作者： Mohammadzadeh, Hekmat Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition-based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.

关键词： email spam detection feature selection flower pollination algorithm opposition-based learning optimization whale optimization algorithm

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