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Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

NEURAL COMPUTING & APPLICATIONS 2021年第23期33卷 16229-16250页

作者： Too, Jingwei Mafarja, Majdi Mirjalili, Seyedali Univ Teknikal Malaysia Melaka Fac Elect Engn Durian Tunggal 76100 Melaka Malaysia Birzeit Univ Fac Engn & Technol Dept Comp Sci Birzeit Palestine Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal number of high-quality features that can maximize the performance of the learning algorithm. However, this problem becomes challenging when the number of features increases in a dataset. Hence, advanced optimization techniques are used these days to search for the optimal feature combinations. whale optimization algorithm (WOA) is a recent metaheuristic that has successfully applied to different optimization problems. In this work, we propose a new variant of WOA (SBWOA) based on spatial bounding strategy to play the role of finding the potential features from the high-dimensional feature space. Also, a simplified version of SBWOA is introduced in an attempt to maintain a low computational complexity. The effectiveness of the proposed approach was validated on 16 high-dimensional datasets gathered from Arizona State University, and the results are compared with the other eight state-of-the-art feature selection methods. Among the competitors, SBWOA has achieved the highest accuracy for most datasets such as TOX_171, Colon, and Prostate_GE. The results obtained demonstrate the supremacy of the proposed approaches over the comparison methods.

关键词： whale optimization algorithm Feature selection Data mining Classification High Dimensional Data optimization Benchmark WOA Swarm intelligence Evolutionary

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Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

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NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 2735-2749页

作者： Zhang, Huiping Wang, Honglun Li, Na Yu, Yue Su, Zikang Liu, Yiheng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Beihang Univ Unmanned Syst Res Inst Beijing 100191 Peoples R China

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

关键词： Trajectory optimization Hypersonic vehicle whale optimization algorithm Gauss pseudo-spectral method

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Code Smell Detection Using whale optimization algorithm

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Computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the whale optimization algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： Software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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

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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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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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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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