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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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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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Improved whale optimization algorithm based on random hopping update and random control parameter

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期40卷 363-379页

作者： Guo, Yanju Shen, Huan Chen, Lei Liu, Yu Kang, Zhilong Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

whale optimization algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

关键词： whale optimization algorithm Meta-heuristic Function optimization Random hopping update Random control parameter

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A Hybrid Strategy Improved whale optimization algorithm for Web Service Composition

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COMPUTER JOURNAL 2023年第3期66卷 662-677页

作者： Ju, Chuanxiang Ding, Hangqi Hu, Benjia Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China

With the rapid growth of the number of web services on the Internet, various service providers provide many similar services with the same function but different quality of service (QoS) attributes. It is a key problem to be solved urgently to select the service composition quickly, meeting the users' QoS requirements from many candidate services. optimization of web service composition is an NP-hard issue and intelligent optimization algorithms have become the mainstream method to solve this complex problem. This paper proposed a hybrid strategy improved whale optimization algorithm, which is based on the concepts of chaos initialization, nonlinear convergence factor and mutation. By maintaining a balance between exploration and exploitation, the problem of slow or early convergence is overcome to a certain extent. To evaluate its performance more accurately, the proposed algorithm was first tested on a set of standard benchmarks. After, simulations were performed using the real quality of web service dataset. Experimental results show that the proposed algorithm is better than the original version and other meta-heuristic algorithms on average, as well as verifies the feasibility and stability of web service composition optimization.

关键词： web services quality of service service composition hybrid strategy whale optimization algorithm

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Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm

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ENERGY CONVERSION AND MANAGEMENT 2018年 174卷 388-405页

作者： Xiong, Guojiang Zhang, Jing Shi, Dongyuan He, Yu Guizhou Univ Guizhou Key Lab Intelligent Technol Power Syst Coll Elect Engn Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. In this paper, an improved whale optimization algorithm (WOA), referred to as IWOA, is proposed to accurately extract the parameters of different PV models. The original WOA has good local exploitation ability, but it is likely to stagnate and suffer from premature convergence when dealing with complex multimodal problems. To conquer this concerning shortcoming, IWOA develops two prey searching strategies to effectively balance the local exploitation and global exploration, and thereby enhance the performance of WOA. Three benchmark test PV models including single diode, double diode and PV module models, and two practical PV power station models with more modules in the Guizhou Power Grid of China are employed to verify the performance of IWOA. The experimental and comparison results comprehensively demonstrate that IWOA is significantly better than the original WOA and three advanced variants of WOA, and is also highly competitive with the reported results of some recently-developed parameter extraction methods.

关键词： Solar photovoltaic Parameter extraction whale optimization algorithm optimization problem

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A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

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NEURAL COMPUTING & APPLICATIONS 2021年第20期33卷 13667-13691页

作者： Stephan, Punitha Stephan, Thompson Kannan, Ramani Abraham, Ajith Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Perak Malaysia Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Washington DC USA

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg-Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

关键词： ANNs Artificial bee colony Multilayer perceptron Levenberg– Marquardt Resilient backpropagation Momentum-based gradient descent backpropagation whale optimization algorithm

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An improved whale optimization algorithm for multi-robot path planning

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ENGINEERING optimization 2024年

作者： Shao, Yijun Zhu, Liangkuan Su, Chunyu Wang, Jingyu Northeast Forestry Univ Sch Comp & Control Engn Harbin Peoples R China Northeast Forestry Univ Sch Mech & Elect Engn Harbin Peoples R China

Multi-robot path planning is challenging and has increasingly attracted attention with its widespread applications. This article proposes an improved whale optimization algorithm (WOA) with Refracted opposition-based learning and Quantum behaviour (RQWOA). The algorithm is able to plan smooth and collisionless paths for robots combining cubic spline interpolation and multi-robot coordination. A quantum behavioural mechanism is used to coordinate the evolution of the whale population during the variable phase to increase the population quality and balance the exploration and exploitation capabilities of the WOA. Simultaneously, refracted opposition-based learning is introduced to improve the algorithm's optimization accuracy and convergence speed. The RQWOA was compared with seven efficient algorithms in experiments on classical test functions and multi-robot path planning cases. The results of these methods were tested statistically. The experimental results indicate that the RQWOA has superior solution accuracy. The RQWOA is highly competitive in terms of pathlength and stability in solving multi-robot path planning problems.

关键词： whale optimization algorithm quantum behaviour refracted opposition-based learning path planning metaheuristic algorithm

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A hybrid whale optimization algorithm for global optimization

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期14卷 431-467页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Sharma, Sushmita Chakraborty, Ratul Debnath, Sudhan Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Natl Inst Technol Dept Math Agartala Tripura India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Maharaja Bir Bikram Coll Dept Chem Agartala Tripura India

Notwithstanding the superior performance of the whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined with the modified mutualism phase of symbiotic organisms search (SOS), DE/rand/1/bin mutation strategy of differential evolution (DE), and commensalism phase of SOS. A new selection parameter gamma is introduced to select between exploration and exploitation phases of the algorithm. This overall arrangement balances the ability of the algorithm to explore or exploit. The algorithm's efficiency is verified through 42 benchmark functions and IEEE CEC 19 test suite and comparing the results with various state-of-the-art algorithms comprising basic methods, WOA variants, and DE variants. Statistical analyses like Friedman's test, box plot comparison, and Nemenyi multiple comparison tests are employed to check the proposed algorithm's consistency and statistical superiority. Finally, four real-life engineering design problems have been solved to confirm the problem-solving capability of the proposed m-SDWOA. All these analyses demonstrate the superiority of the proposed algorithm over the compared algorithms.

关键词： whale optimization algorithm Hybrid algorithm Benchmark functions IEEE CEC 2019 functions Nemenyi multiple comparison test Engineering design problem

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Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第1期17卷 1-27页

作者： Li, Hui Zou, Peng Huang, Zhiguo Zeng, Chenbo Liu, Xiao Beijing Univ Chem Technol Dept Comp Sci & Technol Beijing 100029 Peoples R China

For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization *** whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC'2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.

关键词： whale optimization algorithm local search clustering niching algorithms

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