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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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crayfish optimization algorithm Based Conservation Voltage Reduction Considering Variable Load Events

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-19页

作者： Lakra, Neha Smitha Bag, Baidyanath Natl Inst Technol Raipur Raipur India

The growing demand for sustainable energy has driven the adoption of energy management (EM) strategies to enhance energy efficiency, reduce costs, and improve voltage stability in modern distribution systems. This study presents a conservation voltage reduction (CVR)-based EM scheme that optimally coordinates Volt-Var control (VVC) devices and distributed generation (DG) using the crayfish optimization algorithm (COA) under variable load events, including different loading conditions and future load growth. The presented approach aims to reduce substation demand, reduce costs, and optimized node voltage. The presented approach is verified on the IEEE-69 node system, achieving up to 14.14% substation demand reduction, 45.67% cost savings, and 60% node voltage optimization for normal loading conditions. Additionally, the proposed scheme demonstrates better performance across other loading conditions and future load growth. The efficacy of the COA is compared against other widely used meta-heuristic algorithms, demonstrating its faster convergence and adaptability to variable load events.

关键词： Conservation voltage reduction Capacitor banks Distributed generations crayfish optimization algorithm Energy savings Load growth

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Camera calibration based on hybrid differential evolution and crayfish optimization algorithm

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OPTICS AND LASERS IN ENGINEERING 2025年 193卷

作者： Chen, Zhongqian Yang, Ming Zhang, Jing Zhang, Mengjian Liang, Chengbin Wang, Deguang Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This study proposes a hybrid differential evolution and crayfish optimization algorithm (HDECOA) for precise camera calibration. HDECOA synergizes differential evolution, enhanced by an adaptive parameter control strategy, with crayfish optimization algorithm within a parallel and competitive framework. The hybrid algorithm achieves an effective balance between exploration and exploitation by improving population diversity and optimizing evolutionary efficiency. Finally, HDECOA is applied to calibrate two cameras with distinct parameters. Experimental comparisons evaluate the mean reprojection error of the proposed method against those of methods employing crayfish optimization algorithm, particle swarm optimization, differential evolution, sparrow search algorithm, and Zhang's method. K-means cluster analysis is utilized to evaluate reprojection errors and relative reprojection errors are calculated under varying levels of Gaussian noise. The proposed method achieves mean reprojection errors of 0.054 pixels and 0.166 pixels for the two cameras, respectively. Comprehensive experimental results reveal rapid convergence, high accuracy, robust performance, and versatility of the proposed method, highlighting its superiority over the comparison methods.

关键词： Camera calibration Hybrid algorithm Differential evolution crayfish optimization algorithm K-means cluster analysis

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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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A novel fractional kookaburra crayfish optimization algorithm for trusted routing based blockchain in wireless sensor network

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KNOWLEDGE-BASED SYSTEMS 2025年 323卷

作者： Bagyam, M. Lizzy Nesa Maheswari, S. Kongu Engn Coll Dept EIE Erode 638060 Tamil Nadu India Kongu Engn Coll Dept EEE Erode 638060 Tamil Nadu India

In a Wireless Sensor Network (WSN), the routing procedure is complex and supports data transmission to base stations. However, routing attacks can significantly compromise or disrupt the functionality of WSNs. Furthermore, the majority of routing algorithms are impractical due to the difficulty of effectively determining the reliability of routing nodes. Hence, Fractional Kookaburra crayfish optimization (FKCO) is introduced for trusted routing-based blockchain in WSNs. The WSN is first simulated, and then different factors are taken into consideration to build the node data structure. Afterwards, CH selection is carried out based on a hybrid Kookaburra crayfish optimization algorithm (KCOA) considering multi-objectives. The devised KCOA is an incorporation of the Kookaburra optimization algorithm (KOA) and crayfish optimization algorithm (COA). Subsequently, blockchain-based routing network-based next-hop selection is performed using FKCO based on link reliability, energy prediction, delay, and distance. The FKCO is the amalgamation of the Fractional Concept (FC) with the proposed KCOA. Furthermore, Deep Q-Network (DQN) is used for energy prediction. Further, the FKCO is analyzed for its efficiency by considering three performance metrics, a delay of 0.600 s, energy of 0.400 J, and distance of 48.068 m.

关键词： Blockchain Wireless sensor network Fractional kookaburra crayfish optimization crayfish optimization algorithm Kookaburra 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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Adaptive crayfish optimization algorithm for multi-objective scheduling optimization in distributed production workshops

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Yang, Xin Yang, Xiaoying Du, Jinhao Henan Univ Sci & Technol Sch Business Luoyang 471023 Peoples R China Henan Inst Technol Sch Management Xinxiang 453000 Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Peoples R China Wenzhou Univ Sch Mech & Elect Engn Wenzhou 325035 Peoples R China

The increasing demand for wind turbines and cost pressures in the wind energy industry have made the Wind Turbine Pultruded Panels Production Scheduling Problem (WTPP-PSP) a critical challenge. To address the production scheduling requirements of WTPP-PSP, an intelligent platform is proposed for wind turbine pultruded panel production systems, leveraging intelligent decision-making to tackle the problem. A multi-objective model based on mixed-integer linear programming is developed, considering sequence-dependent completion and setup time constraints. The model aims to maximize customer satisfaction, minimize total setup time, and reduce deviations in workshop machine loads. To solve this problem, an Adaptive crayfish optimization algorithm (ACOA) is introduced. This algorithm incorporates crossover and mutation operators, making it effective for discrete optimization problems. Furthermore, an improved crowding distance calculation enhances the algorithm's performance in multi-objective optimization by improving solution distribution. Reinforcement learning is employed to dynamically adjust temperature parameters, improving both exploration and exploitation capabilities and thus enhancing the convergence of the algorithm. The performance comparison using multi-objective metrics such as HV, IGD, GD, and NR demonstrates that ACOA significantly outperforms COA, WOA, and NSGA-II, with average improvements of 76%, 80%, 28%, and 220%, respectively. These results highlight ACOA's consistent advantages in coverage, convergence, and solution diversity. In the application to WTPP-PSP, the proposed algorithm outperforms COA by approximately 13%, 10%, and 8% in the three objectives.

关键词： Multi-objective optimization crayfish optimization algorithm Reinforcement learning Production scheduling Distributed production workshops

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MCOA: A Multistrategy Collaborative Enhanced crayfish optimization algorithm for Engineering Design and UAV Path Planning

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Xiao, Yaning Cui, Hao Khurma, Ruba Abu Hussien, Abdelazim G. Castillo, Pedro A. Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Al Balqa Appl Univ Al Huson Univ Coll Informat Technol Dept Irbid 19117 Jordan Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Fayoum Univ Fac Sci Faiyum 63514 Egypt Univ Granada Dept Comp Engn Automat & Robot Granada 18071 Spain

The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration-exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to L & eacute;vy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path plann

关键词： crayfish optimization algorithm engineering optimization expanded exploration strategy L & eacute vy flight specular reflection learning vertical crossover operator

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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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