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MSWOA: Multi-strategy whale optimization algorithm for Engineering Applications

ENGINEERING LETTERS 2024年第8期32卷 1603-1615页

作者： Zhou, Ronghe Zhang, Yong Sun, Xiaodong Liu, Haining Cai, Yingying Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China Dagushan Pelletizing Mill Anshan Steel Grp Min Co Anshan 114046 Liaoning Peoples R China Anshan Coll Technol Anshan 114013 Liaoning Peoples R China

The whale optimization algorithm (WOA) is novel algorithm that was motivated by the prey behavior of humpback whales. WOA has attracted a lot of interest due to its few parameters and easy implementation, but it also has sluggish convergence speed, poor convergence accuracy, and is is easy to get a local optima. In this paper, a multi-strategy WOA called MSWOA is constructed to address these drawbacks. It includes dimensional updating, nonlinear convergence factor, global perturbation factor, firefly perturbation, and vertical and horizontal crossover learning strategy. First, a strategy was developed to update each dimension differently to avoid MSWOA from reaching a local optima. Second, a nonlinear convergence factor is devised to better balance the MSWOA's search ability. Third, a global perturbation factor is considered during the exploration phase, to enrich the whale population. Fourth, a firefly perturbation strategy is employed to increase convergence accuracy. Fifth, a vertical and horizontal strategy is applied to accelerate convergence. Finally, twelve CEC2022 benchmark functions and three engineering cases are adopted to evaluate the performance of MSWOA. The results confirm that MSWOA is superior and competitive.

关键词： whale optimization algorithm dimensional updating firefly perturbation vertical and horizontal crossover strategy engineering applications

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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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Potato Yield Prediction Research Based on Improved Artificial Neural Networks Using whale optimization algorithm

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POTATO RESEARCH 2024年 1-10页

作者： Lei, Xue Xu, Xueguo Zhou, Shiyu Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Potato, as a crucial global staple crop, plays a pivotal role in ensuring global food security. In China, both the cultivation area and yield of potatoes rank among the highest globally, highlighting its significance in agricultural production. Accurate prediction of potato yield is essential for guiding cultivation management and making related decisions. The objective of this study is to develop an improved potato yield prediction model to help address supply gaps in the fresh potato market domestically, particularly between northern and southern regions. Considering the limitations of traditional Backpropagation (BP) neural networks in terms of prediction accuracy and robustness, this study optimizes the BP neural network using the whale optimization algorithm (WOA). By analysing meteorological factors, field hydrothermal factors, and potato yield data collected from field Internet of Things (IoT) systems between 2010 and 2022, we constructed and compared three different models: a traditional BP neural network model, a BP neural network model optimized by Genetic algorithm (GA), and a BP neural network model optimized by WOA. The research findings indicate that the WOA-BP model significantly outperforms the other two models in prediction accuracy, with an R2 (coefficient of determination) value of 0.9764. Moreover, the high degree of fit between predicted and observed values validates the scientific validity and accuracy of the WOA-BP model in potato yield prediction.

关键词： Backpropagation neural network Cultivation management Potato yield prediction whale optimization algorithm

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An Efficient Improved whale optimization algorithm for optimization Tasks

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ENGINEERING LETTERS 2024年第2期32卷 392-411页

作者： Wang, Jiayin Wang, Yukun Univ Sci & Technol Liaoning Sch Elect & Engn Anshan 114051 Liaoning Peoples R China

The whale optimization algorithm (WOA) is an efficient meta -heuristic algorithm inspired by the feeding behavior of whales. Although it has successfully solved many optimization problems, it still suffers from premature convergence and poor accuracy when solving complex problems. To address these issues, this paper proposes an improved whale optimization algorithm called EIWOA to improve the search efficiency and accuracy. First, we introduce a new global search mechanism and encircling prey strategy in EIWOA, which utilizes differential evolution, and sine -cosine search strategy to improve the global search efficiency and avoid premature convergence. Second, we use a levy flight -based spiral update position strategy to improve the local search efficiency of the algorithm, thus improving the convergence speed and accuracy. Again, we introduce a balancing factor with fluctuating decay properties into EIWOA to better balance exploration and exploitation. Finally, we introduce a dynamic opposite learning -based whale -fall strategy in EIWOA to equip the algorithm with the ability to jump out of the local optimum. The qualitative analysis of the algorithm shows that the EIWOA algorithm converges quickly, is highly accurate, and has the ability to jump out of the local optimum. In order to validate the performance of the proposed EIWOA algorithm, we evaluated the algorithm on CEC2017 benchmark function and four real world engineering problems. We also conduct a comparative study of the EIWOA algorithm with EWOA, an excellent variant of WOA, as well as some excellent meta- heuristics developed recently. The results of numerical experiments demonstrate the superiority of the proposed EIWOA, which is further confirmed statistically by the results of Friedman's test and Wilcoxon signed rank test. The proposed EIWOA algorithm is significantly better than WOA and other competing algorithms in terms of convergence speed, accuracy and optimization ability in dealing with comp

关键词： whale optimization algorithm exploration and expolitation whale-fall strategy balancing factor

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AWK-TIS:An Improved AK-IS Based on whale optimization algorithm and Truncated Importance Sampling for Reliability Analysis

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Computer Modeling in Engineering & Sciences 2023年第5期135卷 1457-1480页

作者： Qiang Qin Xiaolei Cao Shengpeng Zhang Aerospace Science and Industry Corporation Defense Technology Research and Test Center Beijing100854China Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing100084China

In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural *** novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the *** innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly,and the whale optimization algorithm(WOA)is employed to acquire the optimal Krigingmodel and themost probable point(MPP).To verify the performance of theAWK-TISmethod for structural reliability,four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are *** results indicate the accuracy and efficiency of the proposed method.

关键词： Structural reliability active kriging whale optimization algorithm AK-IS

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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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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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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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Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19959-20000页

作者： Wang, Fengxian Feng, Shaozhi Pan, Youmei Zhang, Huanlong Bi, Senlin Zhang, Jiaxiang Zhengzhou Univ Light Ind Coll Elect & Informat Engn Ke Xue Ave Zhengzhou 450002 Henan Peoples R China

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective.

关键词： whale optimization algorithm Opposition-based learning Dynamic spiral updating Intelligent optimization algorithm Optimum power flow

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Application of improved hybrid whale optimization algorithm to optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12433-12451页

作者： Uzer, Mustafa Serter Inan, Onur Selcuk Univ Ilgin Vocat Sch Elect & Automat Konya Turkiye Selcuk Univ Fac Technol Comp Engn Konya Turkiye

The whale optimization algorithm (WOA) is one of the recent meta-heuristic algorithms. WOA has advantages such as an exploration mechanism that leads towards the global optimum, a suitable balance between exploration and exploitation that avoids the local optimum, and a very good exploitation capability. In this study, five new hybrid algorithms are proposed to develop these advantages. Two of them are developed by combining WOA and Particle Swarm optimization (PSO) algorithms, and three of them are developed by adding the Levy flight algorithm to this combination in different ways. The proposed algorithms have been tested with 23 mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 30 times for each optimization problem. The proposed algorithms' performances were evaluated among themselves, and the WOALFVWPSO algorithm performed better among these algorithms. This proposed algorithm has been first compared with WOA and PSO, then with other algorithms in the literature. According to WOA and PSO, the proposed algorithm performs better in 19 of 23 mathematical optimization problems, and according to other literature, it performs better in 15 of 23 problems. Also, the proposed algorithm has been applied to the pressure vessel design engineering problem and achieved the best result compared to other algorithms in the literature. It has been proven that the WOALFVWPSO algorithm provides competitive solutions for most optimization problems when compared to meta-heuristic algorithms in the literature.

关键词： whale optimization algorithm Particle swarm optimization Levy flight optimization problems Benchmark functions

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