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An Improved whale optimization algorithm for Global optimization and Realized Volatility Prediction

Computers, Materials & Continua 2023年第12期77卷 2935-2969页

作者： Xiang Wang Liangsa Wang Han Li Yibin Guo School of Civil Engineering and Architecture Zhengzhou University of AeronauticsZhengzhou450015China School of Civil and Architectural Engineering Jiangsu University of Science and TechnologyZhenjiang212003China

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal *** address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning *** algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale ***,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during *** work and contributions of this paper are primarily reflected in two ***,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is ***,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)***,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters *** evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced *** experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design *** experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls ***,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting *** comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting *** further confirms that OLCHWOA effectively addresses real-world optimization problems.

关键词： whale optimization algorithm chaos mechanism opposition-based learning long short-term memory realized volatility

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Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 190卷 1342-1369页

作者： Tawhid, M. A. Ibrahim, A. M. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt

This paper suggests a new hybrid algorithm by integrating two population-based algorithms: whale optimization algorithm (WOA) and Flower Pollination algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid whale optimization algorithm and Flower Pollination algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA;WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Hybridization algorithm Metaheuristics whale optimization algorithm Flower pollination algorithm Unconstrained optimization problems System of nonlinear equations

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HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2022年第5期25卷 2265-2295页

作者： Kang, Yan Yang, Xuekun Pu, Bin Wang, Xiaokang Wang, Haining Xu, Yulong Wang, Puming Yunnan Univ Natl Pilot Sch Software Key Lab Software Engn Yunnan Prov Kunming 650500 Yunnan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410006 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China

Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue to increase, the problem of how to optimize task selection based on execution time with limited computing resources becomes a pressing one. To this end, a hybrid whale optimization algorithm (HWOA) is proposed for multi-objective edge computing task selection. In addition to the execution time of the task, economic profits are also considered to optimize task selection. Specifically, a fuzzy function is designed to address the uncertainty of task's economic profits and execution time. Five interactive constraints among tasks are presented and formulated to improve the performance of task selection. Furthermore, some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment. Finally, an extensive experimental assessment of synthetic datasets is implemented to evaluate the multi-objective optimization performance. Compared with the traditional WOA, the diversity metric (A-spread), the hypervolume (HV) and other evaluation metrics are significantly improved. The experiment results also indicate the proposed approach achieves remarkable performance compared with other competitive methods.

关键词： Edge cloud computing system Multi-objective optimization Scheduling Task selection strategy whale optimization algorithm

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Skewed normal cloud modified whale optimization algorithm for degree reduction of S-λ curves

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APPLIED INTELLIGENCE 2021年第11期51卷 8377-8398页

作者： Guo, Wenyan Liu, Ting Dai, Fang Zhao, Fengqun Xu, Peng Xian Univ Technol Sch Sci Xian 710054 Shaanxi Peoples R China

whale optimization algorithm (WOA) is a new meta-heuristic algorithm for mathematical description of the foraging behavior of whales. Cloud model (CM) is an effective model to describe cognitive uncertainty, at the same time, it can comprehensively describe the randomness and fuzziness in uncertain phenomena. Skew normal distribution (SND) can better describe the biological behavior situation when the environment changes. In this paper, a new skew normal cloud model (SNCM) is established in the first place by combining the CM with the SND and the skew normal membership function to describe the fuzziness and randomness of environmental change. Secondly,because of the randomness and fuzziness of the whales' foraging behavior, in order to improve the exploration and exploitation ability of WOA,an emendatory WOA based on the skew normal cloud (SNC) is proposed through the use of the SNCM to modify the shrinking encircling and spiral update strategies of WOA, and the adaptive position and skewness parameters in the SNC are designed to increase the exploration capability in prophase and the exploitation capability in anaphase. Lastly,the experimental results of the complex CEC2017 test set verify the effectiveness of the emendatory WOA under different strategies and different dimensions and different improved WOA algorithm and other representative heuristic algorithms. Three degree reduction of S - lambda curves verify the practicability of the the modified WOA in the field of curve design.

关键词： whale optimization algorithm Skew normal cloud normal CEC2017 benchmark functions S − λ curves

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Power system stability enhancement by damping and control of Sub-synchronous torsional oscillations using whale optimization algorithm based Type-2 wind turbines

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ISA TRANSACTIONS 2021年 108卷 240-256页

作者： Kumar, Rajeev Singh, Rajveer Ashfaq, Haroon Singh, Sudhir Kumar Badoni, Manoj Jamia Millia Islamia Elect Engn Dept New Delhi 110025 India Krishna Inst Engn & Technol Elect & Elect Engn Dept Ghaziabad India Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala Punjab India

This paper is aimed to demonstrate the merits of a metaheuristic swarm-based optimization technique, WOA (whale optimization algorithm), in alleviating the low-frequency torsional oscillations called SSR (Sub-synchronous resonance). The demonstration has been performed using the modified IEEE FBM (IEEE first benchmark model) aggregated with Type-2 WPP (Wind power plant). The Plant is further interlinked to the grid with series compensated lines. Use of WOA for the optimal tuning of the controller suggested in the literature to control one of the degrees of freedom, i.e., Pitch angle and external resistance connected to the rotor, has been demonstrated. The effectiveness of the proposed WOA based controller has been examined using a time-domain approach based on the dynamic response of the different segments of the test system using the Matlab software for the three different cases viz., with Type-2 WPP only, Type-2 WPP with the controller suggested in the literature and with the proposed WOA based controller. The eigenvalues, together with simulation results, reveal the potential of the proposed WOA based controller in damping the low-frequency torsional oscillations using Type-2 wind turbines. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Wind turbine Power system control Pitch control PI controller

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Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 540卷 123094-123094页

作者： Akyol, Sinem Alatas, Bilal Munzur Univ Dept Comp Engn Tunceli Turkey Firat Univ Dept Software Engn Elazig Turkey

In recent years, social networks have become an important part of daily life and are becoming increasingly important. In this study, sentiment analysis or opinion mining, which is one of the most well-known social network analysis problems, is considered as an optimization problem for the first time. In the same way, for the first time, sentiment analysis is considered as a multi-objective problem. whale optimization algorithm and Social Impact Theory based optimization algorithm, which are the current intelligent optimization algorithms, are adapted for the sentiment analysis problem. Furthermore, memory feature is integrated into Social Impact Theory based optimization algorithm in order to obtain effective results in this study. The obtained results from the proposed algorithms are also compared with thirty-three supervised learning algorithms within real IMDB, Polarity, and Amazon data sets. In order to evaluate the performance of the results;accuracy percentage, precision, recall, F-Measure, and MCC that are the five most commonly used measure in the literature are used. When the results are examined, it is observed that adapted metaheuristic optimization algorithms give more successful results in sentiment analysis problem. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Social network analysis Sentiment analysis Opinion mining whale optimization algorithm Social impact theory based optimization algorithm

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A novel energy-aware method for clustering and routing in IoT based on whale optimization algorithm & Harris Hawks optimization

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COMPUTING 2024年第3期106卷 1013-1045页

作者： Heidari, Ehsan Islamic Azad Univ Dept Comp Engn Doroud Branch Doroud Iran

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic whale optimization algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

关键词： Internet of things Wireless sensor networks Clustering Routing Energy consumption whale optimization algorithm Harris Hawks optimization

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A novel image segmentation approach using fcm and whale optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-15页

作者： Tongbram, Simon Shimray, Benjamin A. Singh, Loitongbam Surajkumar Dhanachandra, Nameirakpam Natl Inst Technol Manipur ECE Dept Imphal Manipur India Natl Inst Technol Manipur EE Dept Imphal Manipur India

The segmentation of images is considered a significant step in the processing of images. Due to its simplicity and efficiency, Fuzzy c-means (FCM) is most commonly employed clustering approach for image segmentation. FCM, however, has the drawbacks of sensitiveness to the prior values and local optimum solution and also, it is very sensitive to the effect of noises. In the literature survey, several optimization-based fuzzy clustering approaches were proposed to counter these drawbacks. whale optimization algorithm (WOA) has a strong capability for global optimization and a combination of FCM and WOA has enhanced efficiency over conventional FCM clustering. A new approach to segmentation of image which is based on the WOA and FCM algorithm is proposed in this paper along with the noise detection and reduction mechanism. Since exploration and exploitation phases are performed in nearly equal numbers of iterations separately, the WOA simultaneously shows better avoidance from local optima and superior convergence speed. In our experiment, we have used synthetic images and Medical Resonance Imaging (MRI) Images to validate the performance of the proposed system by taking various types of noise and the findings indicate that the proposed method is more efficient and effectively reduce the impact of noise. We compared the proposed method with other existing clustering-based segmentation techniques and then measured their efficiency using different evaluation indices, and the findings demonstrate the efficacy of the methodology proposed.

关键词： Image segmentation Clustering optimization Fuzzy C-means whale optimization algorithm MRI image

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TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第2期14卷 609-631页

作者： Sun, Lin Wang, Xinya Ding, Weiping Xu, Jiucheng Meng, Huili Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Henan Prov Construct Grp Co Ltd Zhengzhou 450002 Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

The optimal global feature subset cannot be found easily due to the high cost, and most swarm intelligence optimization-based feature selection methods are inefficient in handling high-dimensional data. In this study, a two-stage feature selection model based on fuzzy neighborhood rough sets (FNRS) and binary whale optimization algorithm (BWOA) is developed. First, to denote the fuzziness of samples for mixed data with symbolic and numerical features, fuzzy neighborhood similarity is presented to study the similarity matrix and fuzzy membership degree, and the lower and upper approximations can be developed to present new FNRS model. Fuzzy neighborhood-based uncertainty measures such as dependence degree, knowledge granularity, and entropy measures are studied. From the viewpoints of algebra and information, fuzzy knowledge granularity conditional entropy is presented to form a preselected feature reduction set in the first stage. Second, the cosine curve change is added to develop a new control factor, which slows down the convergence rate of BWOA in the early iteration to fully explore the global, and accelerates the convergence rate in the late iteration. Integrating dependence degree with fuzzy knowledge granularity conditional entropy, a new fitness function is designed for selecting an optimal feature subset in this second stage. Two strategies are fused to avoid BWOA falling into the local optimum: the population partition strategy with the adaptive neighborhood search radius to divide the whale population and the local interference strategy of the elite subgroup to adjust the whale position update. Finally, a two-stage feature selection algorithm is designed, where the Fisher score algorithm is employed to preliminarily delete those redundancy features of high-dimensional datasets. Experiments on six UCI datasets and five gene expression datasets show that our algorithm is valid compared to other related algorithms.

关键词： Feature selection Fuzzy neighborhood whale optimization algorithm Fitness function

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Optimized Multi-User Dependent Tasks Offloading in Edge-Cloud Computing Using Refined whale optimization algorithm

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 14-30页

作者： Hosny, Khalid M. Awad, Ahmed I. Khashaba, Marwa M. Fouda, Mostafa M. Guizani, Mohsen Mohamed, Ehab R. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Masdar City Abu Dhabi U Arab Emirates Zagazig Univ Dept Informat Technol Zagazig 44519 Egypt

Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined whale optimization algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.

关键词： Task analysis Cloud computing Servers Internet of Things Energy consumption Costs whale optimization algorithms multi-access edge computing multi-objective computational offloading multi-user scenario task dependency whale optimization algorithm

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