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MCWOA Scheduler:Modified Chimp-whale optimization algorithm for Task Scheduling in Cloud Computing

Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-whale optimization algorithm called Modified Chimp-whale optimization algorithm(MCWOA),combines elements of the Chimp optimization algorithm(COA)and the whale optimization algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the whale optimization algorithm(WOA),Chimp algorithm(CA),Ant Lion Optimizer(ALO),Genetic algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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Automated Pavement Crack Detection Using Deep Feature Selection and whale optimization algorithm

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Computers, Materials & Continua 2023年第10期77卷 63-77页

作者： Shorouq Alshawabkeh Li Wu Daojun Dong Yao Cheng Liping Li Mohammad Alanaqreh Faculty of Engineering China University of GeosciencesWuhan430074China

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance *** advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains *** this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for *** performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation ***,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two *** results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection *** applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of *** results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement *** leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance ***,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

关键词： Pavement crack detection deep learning feature selection whale optimization algorithm civil engineering

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New binary whale optimization algorithm for discrete optimization problems

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ENGINEERING optimization 2020年第6期52卷 945-959页

作者： Hussien, Abdelazim G. Hassanien, Aboul Ella Houssein, Essam H. Amin, Mohamed Azar, Ahmad Taher Fayoum Univ Fac Sci Faiyum Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Sci Res Grp Egypt SRGE Cairo Cairo Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Menoufia Univ Fac Sci Menoufia Egypt Prince Sultan Univ Coll Engn Riyadh Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way.

关键词： whale optimization algorithm binary whale optimization algorithm optimization problems discrete search space metaheuristic algorithms

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Q-learning whale optimization algorithm for test suite generation with constraints support

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NEURAL COMPUTING & APPLICATIONS 2023年第34期35卷 24069-24090页

作者： Hassan, Ali Abdullah Abdullah, Salwani Zamli, Kamal Z. Razali, Rozilawati Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Comp Pekan 26600 Pahang Malaysia Univ Airlangga Fac Sci & Technol Campus JI Dr H Soekamo C Surabaya 60115 Indonesia

This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms.

关键词： Combinatorial testing Constrained software testing Meta-heuristic Test case generation whale optimization algorithm Reinforcement learning Q-learning algorithm

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A novel community detection method based on whale optimization algorithm with evolutionary population

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APPLIED INTELLIGENCE 2020年第8期50卷 2503-2522页

作者： Feng, Yunfei Chen, Hongmei Li, Tianrui Luo, Chuan Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

Community detection is the process of detecting communities in complex networks. Communities are important structures that can help us further study the properties of complex networks. In recent years, swarm intelligence algorithms have been applied to community detection and have achieved remarkable results. However, these existing algorithms have limited search ability and easily fall into the problem of local optima. In this paper, we propose a new community detection approach based on an improved whale optimization algorithm (WOA). The WOA is applied to a discrete symbol space in solving the community detection problem, therefore topology structure-based search strategies, adjustment and mergence policies, and evolutionary population method are designed to improve the efficiency and effectiveness of the method. Then, a whale optimization algorithm with evolutionary population for community detection (EP-WOCD) is proposed. Extensive experiments are conducted to compare the EP-WOCD with other state-of-the-art algorithms on both artificial and real-world social networks. Experimental results show that the EP-WOCD is effective and stable.

关键词： Complex network Community detection whale optimization algorithm Evolutionary population

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An Adaptive whale optimization algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

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IEEE ACCESS 2019年 7卷 110138-110158页

作者： Li, Yintong Han, Tong Zhao, Hui Gao, Hanjie Air Force Engn Univ Coll Aeronaut Engn Xian 710038 Shaanxi Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130000 Jilin Peoples R China

To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

关键词： whale optimization algorithm CEC 2014 numerical optimization UCAV task allocation

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Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 310卷 123894页

作者： Zhang, Aolin Qin, Ge Wang, Jifen Li, Na Wu, Shihao Peoples Publ Secur Univ China Sch Invest Beijing 102600 Peoples R China Mat Evidence Authenticat & Res Ctr Dezhou Publ Sec Dezhou 253000 Shandong Peoples R China

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted samplingwhale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

关键词： PDE5 inhibitors Drug adulteration Terahertz time-domain spectroscopy Optical parameters whale optimization algorithm

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A novel approach in exergy optimization of a WWER1000 nuclear power plant using whale optimization algorithm

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ANNALS OF NUCLEAR ENERGY 2020年 145卷 107540-107540页

作者： Ebrahimgol, H. Aghaie, M. Zolfaghari, A. Naserbegi, A. Shahid Beheshti Univ Engn Dept GC POB *** Tehran Iran

Nowadays, the scarcity of energy resources and the intensive demand of energy leads the researchers and scientists to find the efficient ways to save the energy and decrease the exergy loss. In this study, a new exergy optimization framework for a typical WWER1000 nuclear power plant (Bushehr NPP) is suggested. It is aimed to optimize the total efficiency of the power plant by decrease the exergy destruction using the first and second laws of thermodynamics. First, the main parameters of Bushehr NPP (BNPP) stream lines and exergy destruction of components are determined and the accuracy of the model is assessed. According to the analysis result, the reactor pressure vessel is the most inefficient equipment in the NPP and the steam generators are the next. Then, the exergy optimization of the steam cycle is evaluated using WOA (whale optimization algorithm). To investigate the performance of the WOA, four general fitness functions are determined and compared with GA and PSO and in next exergy optimization is compared. The total exergy destruction for Bushehr power plant is decreased from 1968.9MWth to 1914.7MWth and the overall thermal efficiency of the BNPP increases from 33.66% to 36.42% using WOA. The results demonstrate that the new algorithm WOA has a proper efficiency to implement in this optimization problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： WWER1000 optimization whale optimization algorithm Exergy destruction

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An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32169-32194页

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

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

关键词： Artificial bee colony algorithm whale optimization algorithm Data clustering optimization

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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 12435-12468页

作者： Abd Elaziz, Mohamed Nabil, Neggaz Moghdani, Reza Ewees, Ahmed A. Cuevas, Erik Lu, Songfeng Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sci & Technol Oran Mohammed Boudiaf USTO MB Fac Math & Informat Dept InformatLab SIMPA BP 1505 El Mnaouer 31000 Oran Algeria Persian Gulf Univ Ind Management Dept Boushehr Iran Damietta Univ Dept Comp Dumyat Egypt Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

关键词： Image segmentation Multilevel thresholding Swarm algorithm Volleyball premier league algorithm whale optimization algorithm

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