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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 2期56卷 1919-1979页

作者： Jia, Heming Rao, Honghua Wen, Changsheng Mirjalili, Seyedali Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Torrens Univ Ctr Artificial Intelligence Res & Optimisat Adelaide SA 5000 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

This paper proposes a meta heuristic optimization algorithm, called crayfish optimization algorithm (COA), which simulates crayfish's summer resort behavior, competition behavior and foraging behavior. The three behaviors are divided into three different stages to balance the exploration and exploitation of algorithm. The three stages are summer resort stage, competition stage and foraging stage. The summer resort stage represents the exploration stage of the COA. The competition stage and foraging stage represent the exploitation stage of the COA. Exploration and exploitation of COA are regulated by temperature. When the temperature is too high, crayfish will enter the cave for summer vacation or compete for the same cave. When the temperature is appropriate, crayfish have different foraging behaviors according to the size of food. Among them, the amount of food eaten by crayfish is related to food intake. Through temperature regulate exploration and exploitation process in COA, the COA has higher randomness and global optimization effect. To verify the optimization effect of COA, in the experimental part, 23 standard benchmark functions and CEC2014 benchmark functions are used to test, and 9 algorithms are selected for comparative experiments. The experimental results show that COA can balance the exploration and exploitation, and achieve good optimization effect. Finally, the COA is tested in five engineering problems, and finally achieves better results. The source code website for COA is https://***/rao12138/COA-s-code.

关键词： crayfish optimization algorithm Summer resort stage Competition stage Foraging stage

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Towards sustainable societies: Convolutional neural networks optimized by modified crayfish optimization algorithm aided by AdaBoost and XGBoost for waste classification tasks

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Tasic, Ana Jovanovic, Luka Bacanin, Nebojsa Zivkovic, Miodrag Simic, Vladimir Popovic, Miroslav Antonijevic, Milos Singidunum Univ Fac Informat & Comp Danijelova 32 Belgrade 11000 Serbia SIMATS Thandalam Saveetha Sch Engn Dept Math Chennai 602105 Tamilnadu India Univ Sinergija Bijeljina 76300 Bosnia & Herceg Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze Univ Coll Engn Dept Ind Engn & Management Taoyuan City 320315 Taiwan Korea Univ Coll Informat Dept Comp Sci & Engn 145 Anam Ro Seoul 02841 South Korea

The categorization of waste is playing a pivotal role in addressing and alleviating the environmental and health repercussions linked to waste. It is essential for safeguarding the environment, as improper handling of hazardous waste may result in soil, water, and air contamination, posing significant threats to ecosystems and human well-being and maintaining a sustainable society. Effective waste classification enhances the efficacy of waste management by organizing waste into distinctive groups based on characteristics that include toxicity, flammability, recyclable potential, and biodegradability. This research introduces a methodology that relies on employing convolutional neural networks and the AdaBoost and XGBoost models for the purpose of waste classification. It emphasizes the necessity of customizing every deep learning method to suit the specific problem that needs to be solved. An altered form of the latterly proposed crayfish optimization algorithm is suggested, explicitly developed to meet the requirements of the particular waste classification task in hand. The assessment of the presented method using real-world datasets consistently demonstrates that models configured by the proposed modified algorithm achieve an accuracy level that exceeds 89.6140%. This pinpoints the considerable potential of this method in effectively addressing pressing problems in waste management within real-world scenarios.

关键词： Waste management Deep neural networks Convolutional neural networks crayfish optimization algorithm Swarm intelligence

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Enhanced crayfish optimization algorithm with differential evolution's mutation and crossover strategies for global optimization and engineering applications

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第3期58卷 1-72页

作者： Maiti, Binanda Biswas, Saptadeep Ezugwu, Absalom El-Shamir Bera, Uttam Kumar Alzahrani, Ahmed Ibrahim Alblehai, Fahad Abualigah, Laith Natl Inst Technol Agartala Dept Math Agartala Tripura India North West Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan

optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces a novel hybrid optimization algorithm, the Hybrid crayfish optimization algorithm with Differential Evolution (HCOADE), which addresses the limitations of premature convergence and inadequate exploitation in the traditional crayfish optimization algorithm (COA). By integrating COA with Differential Evolution (DE) strategies, HCOADE leverages DE's mutation and crossover mechanisms to enhance global optimization performance. The COA, inspired by the foraging and social behaviors of crayfish, provides a flexible framework for exploring the solution space, while DE's robust strategies effectively exploit this space. To evaluate HCOADE's performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 and CEC 2017, as well as six engineering design problems. The results are compared with ten leading optimization algorithms, including classical COA, Particle Swarm optimization (PSO), Grey Wolf Optimizer (GWO), Whale optimization algorithm (WOA), Moth-flame optimization (MFO), Salp Swarm algorithm (SSA), Reptile Search algorithm (RSA), Sine Cosine algorithm (SCA), Constriction Coefficient-Based Particle Swarm optimization Gravitational Search algorithm (CPSOGSA), and Biogeography-based optimization (BBO). The average rankings and results from the Wilcoxon Rank Sum Test provide a comprehensive comparison of HCOADE's performance, clearly demonstrating its superiority. Furthermore, HCOADE's performance is assessed on the CEC 2020 and CEC 2022 test suites, further confirming its effectiveness. A comparative analysis against notable winners from the CEC competitions, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES, using the CEC-2017 test suite, revealed superior results for HCOADE. This study underscores the advantages of integrating DE strategies with COA and offers valuable insigh

关键词： crayfish optimization algorithm Differential evolution Global optimization Mutation strategy Crossover strategy Benchmark functions CEC competitions Engineering applications

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Multi-strategy improvement of crayfish optimization algorithm to solve high-dimensional feature selection

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-33页

作者： Shi, Xiaoming Jia, Heming Rao, Honghua You, Fangkai Abualigah, Laith Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Sanming Univ Fujian Key Lab Agr IOT Applicat Sanming 365004 Peoples R China Northeast Petr Univ Sch Elect & Informat Engn Daqing 163318 Peoples R China Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Ctr Res Impact & Outcome Rajpura 140401 Punjab India Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

In 2023, the crayfish optimization algorithm (COA) was introduced as a type of meta-heuristic optimization algorithm, inspired by crayfish behavior. While COA exhibits strong optimization performance, it encounters challenges such as slow convergence and susceptibility to local optima. To address these issues, this paper introduces the Multi-strategy improvement of crayfish optimization algorithm to solve high-dimensional feature selection (MICOA), building upon COA's foundation. Specifically, MICOA enhances the original algorithm by introducing the cave selection strategy, mitigating the tendency to converge to local optima. Additionally, it incorporates a food attraction strategy based on crayfish behavior, fostering a more balanced exploration-exploitation trade-off and enhancing convergence speed. The paper verifies MICOA's performance against the original COA and six comparison algorithms. Results on CEC2020 and CEC2014 demonstrate MICOA's enhanced ability to overcome local optima and converge faster. In terms of data analysis, the enhanced MICOA demonstrates an average 20% enhancement in convergence capability compared to the original COA. Furthermore, when contrasted with alternative algorithms, MICOA showcases a maximum improvement of 75%. Applied to high-dimensional feature selection datasets, MICOA outperforms the original algorithm and six other methods, achieving the highest accuracy on most datasets. MICOA provides a significant 70% improvement in accuracy compared to the original COA for feature selection applications. In addition, MICOA has a significant advantage over the prevalent feature selection algorithms currently in use. These results highlight the superiority of MICOA over existing methods.

关键词： crayfish optimization algorithm Meta-heuristic optimization algorithm Feature selection problem Cave selection strategy Food attraction strategy Cauchy mutation strategy

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Modified crayfish optimization algorithm for solving multiple engineering application problems

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第5期57卷 127-127页

作者： Jia, Heming Zhou, Xuelian Zhang, Jinrui Abualigah, Laith Yildiz, Ali Riza Hussien, Abdelazim G. Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Bursa Uludag Univ Dept Mech Engn TR-16059 Gorukle Bursa Turkiye Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden

crayfish optimization algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization.

关键词： crayfish optimization algorithm Environmental updating mechanism Ghost opposition-based learning strategy Global optimization problem Constrained engineering design problems High dimensional feature selection

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Prediction of Liquid Accumulation Height in Gas Well Tubing Using Integration of crayfish optimization algorithm and XGBoost

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PROCESSES 2024年第9期12卷 1788页

作者： Xia, Wenlong Liu, Botao Xiang, Hua Yangtze Univ Hubei Key Lab Oil & Gas Drilling & Prod Engn Jingzhou Peoples R China

The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the crayfish optimization algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA-XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA-XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells.

关键词： gas well fluid accumulation prediction model machine learning XGBoost crayfish optimization algorithm

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Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第4期11卷 249-305页

作者： Li, Guanghui Zhang, Taihua Tsai, Chieh-Yuan Lu, Yao Yang, Jun Yao, Liguo Guizhou Normal Univ Sch Mech & Elect Engn Dept Mech Engn Guiyang 550025 Guizhou Peoples R China Guizhou Normal Univ Tech Engn Ctr Mfg Serv & Knowledge Engn Guiyang 550025 Guizhou Peoples R China Yuan Ze Univ Dept Ind Engn & Management Taoyuan 32003 Taiwan

crayfish optimization algorithm (COA) is a novel bionic metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test value in 23 test functions, CEC2014 and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the WT, respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity. Graphical Abstract

关键词： crayfish optimization algorithm search-hide opposition-based learning competition-elimination chaos mutation engineering optimization

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Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Ragab, Mahmoud Asar, Turky Omar King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia Univ Jeddah Coll Sci & Arts Alkamil Dept Biol Jeddah Saudi Arabia

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a gre

关键词： Oral cancer Bilateral filtering crayfish optimization algorithm Histopathological image Bidirectional long short-term memory Squeeze-excitation- CapsNet

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Self-Attention conditional generative adversarial network optimised with crayfish optimization algorithm for improving cyber security in cloud computing

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COMPUTERS & SECURITY 2024年 140卷

作者： Jose, G. Sahaya Stalin Sugitha, G. Lakshmi, S. Ayshwarya Chaluvaraj, Preethi Bangalore St Xaviers Catholic Coll Engn Dept Informat Technol Chunkankadai Nagercoil India Muthayammal Engn Coll Dept Comp Sci & Engn Rasipuram India Univ Coll Engn Dept Comp Sci & Engn Panruti Tamil Nadu India St Xaviers Catholic Coll Engn Dept Elect & Commun Engn Nagercoil Tamil Nadu India

The decentralized and distributed architecture of cloud computing promotes adoption and growth in various societal domains, including education, government, information technology, business, entertainment. Cloud computing (CC) makes a broad range of information technologies available. Security and privacy are key challenges in storing big data in the cloud. To overcome this challenge, a Self-Attention Conditional Generative Adversarial Network Optimised with crayfish optimization algorithm for Improving Cyber Security in Cloud Computing (CybS-CC-SACGAN-COA) is proposed in this paper to enhance the safety of CC environment. The input data is amassed from NSL-KDD database. After that, the data is fed to pre-processing segment. The segment of pre-processing eliminates Cloud data termination and missing value replacement using Reformed Phase Conserving Vibrant Range Compression filter. The results of pre-processing serves for feature selection. The optimal features are selected by means of Manta Ray Foraging optimization algorithm (MRFOA). Cloud data is categorized as normal and abnormal data, like Denial-of-Service (DoS), Probe, Remote to local attack (R2L), User to root attack (U2R) by the help of SACGAN. crayfish optimization algorithm (COA) is proposed to optimize the SACGAN classifier that classifies anomaly data precisely. The proposed CSCC-SACGAN-COA technique is activated in Python under some metrics. The proposed CSCC-SACGAN-COA approach has attained higher detection accuracy, lower computation time, higher AUC, higher scalability and lower detection error rate compared to the existing methods.

关键词： Cloud computing crayfish optimization algorithm NSL-KDD benchmark dataset Reformed phase conserving vibrant range compression filter Manta ray foraging optimization algorithm and self -attention conditional generative adversa rial network

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Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Yan, Dongmei Wang, Hongkun Gao, Yujie Tian, Shiji Zhang, Hong Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China Xinjiang Prod & Construct Corps Key Lab Modern Ag Shihezi 832003 Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Agr Equipment Shihezi 832003 Peoples R China

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum crayfish optimization algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

关键词： Multi-microgrid system Optimal scheduling crayfish optimization algorithm Shared energy storage Stackelberg game

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