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A process prediction model based on cuckoo algorithm for abrasive waterjet machining

JOURNAL OF INTELLIGENT MANUFACTURING 2015年第6期26卷 1247-1252页

作者： Mohamad, Azizah Zain, Azlan Mohd Bazin, Nor Erne Nazira Udin, Amirmudin Univ Teknol Malaysia Fac Comp Soft Comp Res Grp Skudai 81310 Kagawa Malaysia Univ Teknol Malaysia Fac Educ Skudai 81310 Kagawa Malaysia

cuckoo is a new evolutionary algorithm which is inspired by lifestyle of bird family. This study proposes cuckoo algorithm for prediction of the surface roughness of Abrasive Water Jet (AWJ). Several prediction models with different initial eggs were developed and analyzed to investigate the best predicted surface roughness value. The paired sample t-test was used to demonstrate the validity of the results. Throughout this study, it was evidence that the cuckoo algorithm could improve machining performances of the AWJ. The result shows that the more initial eggs were considered, a much lower predicted value of surface roughness was obtained. When the initial eggs increase, the value of the best parameters become nearer to the goal point. It was found that cuckoo algorithm is capable for giving an improved surface roughness as it outperformed the results of two established computational techniques, artificial neural network and support vector machine.

关键词： cuckoo algorithm Surface roughness Abrasive water jet Machining

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An Improved cuckoo Search algorithm for Target Assignment

An Improved Cuckoo Search Algorithm for Target Assignment

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2017 the 2nd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2017)

作者： Yunzhi Zhang Gang Wang Xiaoyang Gao Shengli Liu Tianxiang Gao School of Air and Missile Defense Air Force Engineering University

ISBN: (纸本)9781510840683

The purpose is to study an improved cuckoo search algorithms in target assignment via the analysis of the characteristics of air defense interception and the demand for weapon target assignment（WTA）.As for the threat of multi-batches and saturating targets,the random coding method based on the minimum position rule is used to initialize the *** addition,the convergence are ensured by adding adaptive communication and mutation *** improved algorithm is then simulated and analyzed by the air defense target interception case,and the results indicate that the improved algorithm is effectiveness and superiority in solving the problem of WTA,compared to the standard cuckoo and immune *** current study indicate that the improved algorithm provides a kind of effective method for solving the problem of WTA.

关键词： WTA cuckoo algorithm adaptive mutation operator adaptive communication operator

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Presenting a model in smart electronic health networks based on IoT-Fog for health care to optimize resources

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ELECTRICAL ENGINEERING 2025年第1期107卷 1125-1140页

作者： Kang, Yingyun Hangzhou Vocat & Tech Coll Fair Friend Inst Intelligent Mfg Hangzhou 310018 Peoples R China

There have been huge improvements in healthcare delivery thanks to sensing and the Internet of Things (IoT). By utilizing cloud resources, the Internet of Things can broaden its reach. Contrarily, data gathered from healthcare IoTs can also help cloud improve its services. Because of the physical distance between the cloud and the Internet of Things, it is difficult to provide services that have special needs, such as low computational processing requirements or fast reaction times. Using the cuckoo optimization algorithm, this research introduces a novel approach to optimizing simulated e-health smart networks. The SHE-cuckoo optimization algorithm (SHE-COA) is the name of this specific approach. cuckoo algorithm plays a crucial part in SHE-COA by assisting with the optimization of the search space size in order to attain the optimal solution. The outcomes of the current strategy were compared to those of the proposed one using the iFogSim application. When this study's methodology is compared to earlier ones, the experimental results reveal a notable decrease in energy usage, network bandwidth utilization, and latency. In particular, the suggested method reduces latency by 41%, energy consumption by 38%, and network bandwidth utilization by 23%.

关键词： Heart patient analysis Smart health care Fog computing IoT cuckoo algorithm

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Offshore wind power prediction based on two-stage hybrid modeling

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ENERGY STRATEGY REVIEWS 2024年 54卷

作者： Liang, Jiajia He, Xiaoxia Xiao, Haoyi Wu, Chuanju Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Met Ind Proc Syst Sci Wuhan Peoples R China

Accurate prediction of offshore wind power generation is essential for efficient power scheduling and grid integration. This study introduces an innovative hybrid forecasting approach to address variability in power output due to different climatic conditions and challenges in measuring offshore wind speeds. The approach consists of two primary phases. First, a gated recurrent unit neural network with an attentional mechanism and a quantile regression model forecast wind speeds between cut-in and rated wind speeds, capturing trends under distinct climatic conditions. Second, wind speeds at nine quantile points are used as inputs to a relevance vector machine model, optimized via a cuckoo search algorithm, to establish the relationship between wind speed and power output. An empirical evaluation on a European offshore wind power dataset validates the approach's effectiveness across various climatic conditions. The two-stage model demonstrates enhanced adaptability, offering more reliable power predictions than conventional methods. Results indicate this hybrid forecasting method is more accurate than traditional techniques, significantly improving offshore wind power prediction performance.

关键词： Offshore wind power Gated recurrent unit Attention mechanism Quantile regression Correlation vector machine cuckoo algorithm

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A novel sparrow search algorithm with integrates spawning strategy

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1753-1773页

作者： Zhang, Jiaxiang Li, Linwei Zhang, Huanlong Wang, Fengxian Tian, Yangyang Zhengzhou Univ Light Ind Coll Elect & Informat Engn 136 Ke Xue Ave Zhengzhou 450000 Henan Peoples R China State Grid Henan Elect Res Inst 85 Songshan South Rd Zhengzhou 450000 Henan Peoples R China

Sparrow Search algorithm (SSA) has recently received more attention in the intelligent optimization group. However, the high randomness and local optimal problems of the algorithm limit the application of the algorithm. In this paper, a novel sparrow search algorithm (NSSA) combined with spawning technology is proposed, which effectively improves the problem of the algorithm falling into local optimization. First of all, it is proposed to replace the traditional stochastic method with the good point set theory to find the initial individual, so that the initial population is more evenly distributed in the search space and improve the quality of the initial solution. Secondly, the spawning strategy of the cuckoo algorithm is integrated into the discoverer stage, which improves the search method of the discoverer and enhances the global search ability, which makes the algorithm avoids the sudden decline of population diversity and premature convergence. Finally, Levy flight and Brownian motion are used to disturb the position of the sparrow dimension by dimension to improve the ability of the algorithm to jump out of the local optimal value in subsequent iterations. By comparing and analyzing NSSA and SSA, DE, ALO, GOA, DOA, HSSA and XSSA algorithms in 12 common evaluation functions and CEC-2017 test functions, and apply NSSA to the TSP problem. The simulation results show the effectiveness and superiority of the proposed scheme.

关键词： Sparrow search algorithm Optimization scheme cuckoo algorithm Local optimum problem High randomicity

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An Efficient Caching and Offloading Resource Allocation Strategy in Vehicular Social Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第4期73卷 5690-5703页

作者： Zhang, Yuexia Zhou, Ying Zhang, Siyu Gui, Guan Adebisi, Bamidele Gacanin, Haris Sari, d Hikmet Beijing Informat Sci & Technol Univ Minist Informat Ind Minist Educ Key Lab Informat & Commun Syst Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Lab Optoelect Measurement Technol & Instrument Minist Educ Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Educ Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Manchester Metropolitan Univ Fac Sci & Engn Dept Engn Manchester M1 5GD England Rhein Westfal TH Aachen Fac Elect Engn & Informat Technol D-5552062 Aachen Germany

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing.

关键词： cuckoo algorithm resource allocation task offloading vehicular social networks

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Energy-aware scheduling protocol-based hybrid metaheuristic technique to optimize the lifespan in WSNs

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12706-12726页

作者： Hameed, Mazin Kadhum Idrees, Ali Kadhum Univ Babylon Dept Software Babylon Iraq Univ Babylon Coll Informat Technol Dept Informat Networks Babylon Iraq

Topology control is a significant research problem when it comes to designing energy-efficient wireless sensor networks (WSNs). It is a metric for evaluating network service quality. To ensure the quality of network services, it is critical to provide energy-efficient sensor node scheduling in order to extend the network's lifespan. In this paper, we propose an energy-aware scheduling protocol (ESP)-based hybrid metaheuristic technique for minimizing the amount of energy consumption and ensuring sufficient coverage for the monitored area while maximizing the network lifespan for WSNs. The ESP consists of two phases: distributed clustering and sensor node scheduling. The clustering phase implements the DBSCAN clustering algorithm with slide modification to cluster the sensor nodes in the area of interest into clusters of nodes, and the efficient periodic cluster head election approach is proposed based on criteria such as remaining energy, number of neighbors, and distance for each node in the cluster. This clustering is only performed at the beginning to group the nodes into clusters. Then, only the cluster head election will be done every period. In the scheduling phase, we model the scheduling optimization problem using three objectives: reducing the number of uncovered zones, minimizing the number of active sensors, and selecting the active sensor nodes with the maximum remaining energy. This scheduling optimization model is solved using a hybrid metaheuristic algorithm. When the genetic algorithm's operators were added to the regular cuckoo algorithm, the balance between exploration and exploitation abilities was improved even more. The algorithms were able to search a larger area. Extensive simulation experiments were conducted using the OMNeT++ network simulator. The results show that the proposed ESP protocol introduces better performance in terms of coverage ratio, active sensor ratio, energy consumption, network lifespan, and execution time compared to othe

关键词： Wireless sensor networks cuckoo algorithm Genetic algorithm Clustering Scheduling Lifetime enhancement

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Hybrid Location algorithm Based on cuckoo and Statistical Manifold

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Journal of Computer and Communications 2021年第3期9卷 110-117页

作者： Xiaofeng Qin Bin Xia Liye Zhang Xianzhi Zheng School of Computer Science and Technology Shandong University of Technology Zibo China

To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold’s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.

关键词： Hybrid Location algorithm Statistical Manifold Method cuckoo algorithm Location Accuracy

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Improving long short-term memory forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2552-e2552页

作者： Zhu, Mingfu Liu, Yaxing Qin, Panke Ding, Yongjie Cai, Zhongqi Gao, Zhenlun Ye, Bo Qi, Haoran Cheng, Shenjie Zeng, Zeliang Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo Henan Peoples R China Hebi Natl Optoelect Technol Co Ltd Hebi Henan Peoples R China

Long short-term memory (LSTM) networks, widely used for financial time forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy. We propose an integrated cuckoo and Zebra algorithms-optimised (ICS-LSTM) network for arbitrage spread prediction. This method replaces the flight in the cuckoo algorithm with the Zebra algorithm search, improving convergence speed and solution optimization. Experimental results showed absolute percentage error (MAPE) of 0.011, mean square error (MSE) of 3.326, absolute error (MAE) of 1.267, and coefficient of determination (R2) of 0.996. proposed model improved performance by reducing MAPE by 8.3-50.0%, MSE 10.2-77.8%, and MAE by 9.3-63.0% compared to existing methods. These improvements translate to more accurate spread predictions, enhancing arbitrage opportunities and trading strategy profitability.

关键词： LSTM networks Arbitrage spread forecasting Hyperparameter setting cuckoo algorithm Zebra algorithm

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Parameter setting of meta-heuristic algorithms: a new hybrid method based on DEA and RSM

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第15期29卷 22404-22426页

作者： Shadkam, Elham Khayyam Univ Fac Engn Dept Ind Engn Mashhad Razavi Khorasan Iran

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.

关键词： Meta-heuristic algorithms Parameter setting cuckoo algorithm Response surface methodology Data envelopment analysis Waste management COVID-19

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