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Nearest neighbour cuckoo search algorithm with probabilistic mutation

APPLIED SOFT COMPUTING 2016年 49卷 498-509页

作者： Wang, Lijin Zhong, Yiwen Yin, Yilong Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250101 Peoples R China

In this study, we present a nearest neighbour cuckoo search algorithm with probabilistic mutation, called NNCS. In the proposed approach, the nearest neighbour strategy is utilized to select guides to search for new solutions by using the nearest neighbour solutions instead of the best solution obtained so far. In the proposed strategy, we respectively employ a solution-based and a fitness-based similar metrics to select the nearest neighbour solutions for implementation. Furthermore, the probabilistic mutation strategy is used to control the new solutions learn from the nearest neighbour ones in partial dimensions only. In addition, the nearest neighbour strategy helps the best solution participate in searching too. Extensive experiments, which are carried on 20 benchmark functions with different properties, demonstrate the improvement in effectiveness and efficiency of the nearest neighbour strategy and the probabilistic mutation strategy. (C) 2016 Elsevier B.V. All rights reserved.

关键词： cuckoo search algorithm Nearest neighbour Solution-based similar metric Fitness-based similar metric Probabilistic mutation

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Fuel flow rate modeling for descent using cuckoo search algorithm: a case study for point merge system procedure at Istanbul airport

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2022年第5期94卷 824-833页

作者： Oruc, Ridvan Sahin, Ozlem Baklacioglu, Tolga Kastamonu Univ Sch Civil Aviat Dept Airframe & Powerplant Maintenance Kastamonu Turkey Eskisehir Tech Univ Fac Aeronaut & Astronaut Dept Air Traff Control Eskisehir Turkey Eskisehir Tech Univ Fac Aeronaut & Astronaut Dept Airframe & Powerplant Maintenance Eskisehir Turkey

Purpose The purpose of this paper is to create a new fuel flow rate model using cuckoo search algorithm (CSA) for the descending stage of the flight. Design/methodology/approach Using the actual flight data record data of the B737-800 aircraft, a new fuel flow rate model has been developed for this aircraft type. The created model is to predict the fuel flow rate with high accuracy depending on the altitude and true airspeed. In addition, the CSA fuel flow rate model was used to calculate the fuel consumption for the point merge system, which is used for combining the initial approach to the final approach at Istanbul Airport, the largest airport of Turkey. Findings As a result of the analysis, the correlation coefficient value is found as 0.996858 for Flight 1, 0.998548 for Flight 2, 0.995363 and 0.997351 for Flight 3 and Flight 4, respectively. The values that are so close to 1 indicate that the model predicts the real fuel flow rate data with high accuracy. Practical implications This model is considered to be useful in air traffic management decision support systems, aircraft performance models, models used for trajectory prediction and strategies used by the aviation community to reduce fuel consumption and related emissions. Originality/value The importance of this study lies in the fact that to the best of the authors' knowledge, it is the first fuel flow rate model developed using CSA for the descent stage in the existing literature;the data set used is real values.

关键词： Aircraft Fuel flow rate Descent cuckoo search algorithm Optimization Point merge system

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Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2020年第3期92卷 495-501页

作者： Oruc, Ridvan Baklacioglu, Tolga Kastamonu Univ Sch Civil Aviat Dept Airframe & Powerplant Maintenance Kastamonu Turkey Eskisehir Tech Univ Fac Aeronaut & Astronaut Eskisehir Turkey

Purpose The purpose of this study is to create a new fuel flow rate model adopting cuckoo search algorithm (CSA) for the climbing phase of the flight. Design/methodology/approach Using the real flight data records (FDRs) of B737-800 passenger aircraft, a new fuel flow rate model for the climbing phase of the flight was developed by incorporating CSA. In the model, fuel flow rate is given as a function of altitude and true airspeed. The aim is to create a model that yields results that are closest to the real fuel flow rate values obtained from flight data records. Various error analysis methods were used to test the accuracy of the obtained values. Finally, the effect of change of some CSA parameters on the model was investigated. Findings It was observed that the derived model is able to predict real fuel flow rate values with high accuracy. It has been deduced that increasing the number of nest (n) and discovery rate of alien nests (pa) values of CSA parameters to a certain value gradually decreases the model's accuracy. Originality/value The originality of this study is the development of a new fuel flow rate model using CSA as a first attempt in the literature. The use of real flight data is important for the originality and reliability of the model.

关键词： Aircraft Fuel flow rate prediction Climb flight cuckoo search algorithm

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CSA-WSC: cuckoo search algorithm for web service composition in cloud environments

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SOFT COMPUTING 2018年第24期22卷 8353-8378页

作者： Ghobaei-Arani, Mostafa Rahmanian, Ali Asghar Aslanpour, Mohammad Sadegh Dashti, Seyed Ebrahim Islamic Azad Univ Qom Branch Dept Comp Engn Qom Iran Shiraz Univ Dept Comp Sci & Engn & IT Coll Elect & Comp Engn Shiraz Iran Islamic Azad Univ Jahrom Branch Dept Comp Engn Jahrom Iran

In recent years, service-based applications are deemed to be one of the new solutions to build an enterprise application system. In order to answer the most demanding needs or adaptations to the needs of changed services quickly, service composition is currently used to exploit the multi-service capabilities in the Information Technology organizations. While web services, which have been independently developed, may not always be compatible with each other, the selection of optimal services and composition of these services are seen as a challenging issue. In this paper, we present cuckoo search algorithm for web service composition problem which is called CSA-WSC' that provides web service composition to improve the quality of service (QoS) in the distributed cloud environment. The experimental results indicate that the CSA-WSC compared to genetic search skyline network (GS-S-Net) and genetic particle swarm optimization algorithm (GAPSO-WSC) reduces the costs by 7% and responding time by 6%, as two major reasons for the reduction of improvement of the quality of service. It also increases provider availability up to 7.25% and the reliability to 5.5%, as the two important QoS criteria for improving the quality of service.

关键词： Cloud computing Web service composition Quality of service cuckoo search algorithm

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Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm

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APPLIED SOFT COMPUTING 2017年 61卷 149-173页

作者： Chiroma, Haruna Herawan, Tutut Fister, Iztok, Jr. Fister, Iztok Abdulkareem, Sameem Shuib, Liyana Hamza, Mukhtar Fatihu Saadi, Younes Abubakar, Adamu Fed Coll Educ Tech Dept Comp Sci Gombe Nigeria Univ Malaya Fac Comp Sci & IT Kuala Lumpur Malaysia Univ Maribor Fac Elect Engn & Comp Sci Maribor Slovenia Bayero Univ Kano Fac Engn Kano Nigeria Int Islamic Univ Kuala Lumpur Malaysia

Presently, the cuckoo search algorithm is attracting unprecedented attention from the research community and applications of the algorithm are expected to increase in number rapidly in the future. The purpose of this study is to assist potential developers in selecting the most suitable cuckoo search variant, provide proper guidance in future modifications and ease the selection of the optimal cuckoo search parameters. Several researchers have attempted to apply several modifications to the original cuckoo search algorithm in order to advance its effectiveness. This paper reviews the recent advances of these modifications made to the original cuckoo search by analyzing recent published papers tackling this subject. Additionally, the influences of various parameter settings regarding cuckoo search are taken into account in order to provide their optimal settings for specific problem classes. In order to estimate the qualities of the modifications, the percentage improvements made by the modified cuckoo search over the original cuckoo search for some selected reviews studies are computed. It is found that the population reduction and usage of biased random walk are the most frequently used modifications. This study can be used by both expert and novice researchers for outlining directions for future development, and to find the best modifications, together with the corresponding optimal setting of parameters for specific problems. The review can also serve as a benchmark for further modifications of the original cuckoo search. (C) 2017 Elsevier B.V. All rights reserved.

关键词： cuckoo search algorithm Convergence Swarm intelligence Nature-inspired algorithms Optimization metaheuristics

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A cuckoo search algorithm optimizer for steady-state analysis of self-excited induction generator

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AIN SHAMS ENGINEERING JOURNAL 2018年第4期9卷 2549-2555页

作者： Hasanien, Hany M. Hashem, Gamal M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

Renewable energy systems especially the wind energy grows worldwide continuously. Also, installations of the wind farms increase in remote area applications. The self-excited induction generator (SEIG) is an effective choice for using in these applications due to its merits. This paper presents a cuckoo search algorithm (CSA) optimizer for steady state analysis of the SEIG. The mathematical model of the SEIG is based on equivalent circuit loop impedance method. The target of optimization is to minimize the total equivalent circuit impedance of the generator in order to obtain the frequency and any other unknown parameter of the SEIG. In this study, the frequency and magnetizing reactance define the search space for the optimization problem. The MATLAB CSA optimization toolbox is used to solve optimization problem. The effectiveness of the proposed optimizer is evaluated on a 220 V, 0.75 kW induction generator for various operating conditions. The effectiveness of the CSA model is then compared with other conventional optimization models. The validity of the proposed optimizer is verified by experimental results. With this proposed CSA optimization technique, a faster and an accurate prediction for steady state analysis of the SEIG are achieved. (C) 2017 Ain Shams University.

关键词： cuckoo search algorithm Optimization Self-excited induction generator Steady-state analysis Wind energy

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Application Layer DDoS Attack Detection Using cuckoo search algorithm-Trained Radial Basis Function

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IEEE ACCESS 2022年 10卷 63844-63854页

作者： Beitollahi, Hakem Sharif, Dyari Mohammad Fazeli, Mahdi Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Soran Univ Comp Sci Dept Soran 44008 Kurdistan Regio Iraq Halmstad Univ Sch Informat Technol S-30118 Halmstad Sweden

In an application-layer distributed denial of service (App-DDoS) attack, zombie computers bring down the victim server with valid requests. Intrusion detection systems (IDS) cannot identify these requests since they have legal forms of standard TCP connections. Researchers have suggested several techniques for detecting App-DDoS traffic. There is, however, no clear distinction between legitimate and attack traffic. In this paper, we go a step further and propose a Machine Learning (ML) solution by combining the Radial Basis Function (RBF) neural network with the cuckoo search algorithm to detect App-DDoS traffic. We begin by collecting training data and cleaning them, then applying data normalizing and finding an optimal subset of features using the Genetic algorithm (GA). Next, an RBF neural network is trained by the optimal subset of features and the optimizer algorithm of cuckoo search. Finally, we compare our proposed technique to the well-known k-nearest neighbor (k-NN), Bootstrap Aggregation (Bagging), Support Vector Machine (SVM), Multi-layer Perceptron) MLP, and (Recurrent Neural Network) RNN methods. Our technique outperforms previous standard and well-known ML techniques as it has the lowest error rate according to error metrics. Moreover, according to standard performance metrics, the results of the experiments demonstrate that our proposed technique detects App-DDoS traffic more accurately than previous techniques.

关键词： Servers Feature extraction Genetic algorithms Denial-of-service attack Support vector machines Computer crime Load modeling Application layer DDoS machine learning radial basis function cuckoo search algorithm genetic algorithm

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5G heterogeneous network selection and resource allocation optimization based on cuckoo search algorithm

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COMPUTER COMMUNICATIONS 2021年 168卷 170-177页

作者： Ai, Ning Wu, Bin Li, Boyu Zhao, Zhipeng Tianjin Univ Sch Comp Sci & Technol Tianjin 300000 Peoples R China

In order to solve the problem of spectrum resource shortage and high-speed access in 5G network, the multi-agent system is embedded into the standard cuckoo algorithm, and the multi-agent cuckoo algorithm is proposed. Firstly, the spectrum information of network channel sharing available to users is obtained, and the cuckoo search algorithm is used to optimize under the condition of satisfying the quality of service (QoS) guarantee of users, and the optimal allocation scheme is obtained by iterating for many times. The use steps are illustrated by examples. Compared with the traditional genetic algorithm, the calculation complexity can be reduced, and it can also be extended to more users and networks. In this algorithm, each cuckoo represents an agent, and all the agents constitute a von-Neumann structure. Through the neighborhood competition cooperation operator, mutation operator, self-learning operator and the evolution mechanism of cuckoo algorithm, they can continuously enhance the energy and improve the adaptability, and can quickly and accurately find the optimal solution of the problem.

关键词： cuckoo search algorithm 5G heterogeneous network Resource allocation Optimization

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Optimisation of antenna arrays using the cuckoo search algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2013年第6期7卷 458-464页

作者： Khodier, Majid Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

The goal of this article is to introduce and use the cuckoo search (CS) as an optimisation algorithm for the electromagnetics and antenna community. The CS is a new nature-inspired evolutionary algorithm (EA) for solving N-dimensional optimisation problems. Compared with other nature-inspired algorithms, the CS algorithm is easy to understand and implement and has minimum number of parameters to tune. Different examples are presented that illustrate the use of the CS algorithm, and the results are compared with results obtained using other optimisation methods. Preliminary results suggest that the CS algorithm can in some cases outperform other EAs, at least for the examples studied in this article.

关键词： antenna arrays evolutionary computation optimisation search problems antenna array optimisation cuckoo search algorithm electromagnetics community antenna community N-dimensional optimisation problems nature-inspired evolutionary algorithm CS algorithm

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Design and Optimization of Enhanced Magnitude and Phase Response IIR Full-Band Digital Differentiator and Integrator Using the cuckoo search algorithm

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IEEE ACCESS 2022年 10卷 28938-28948页

作者： Ababneh, Jehad, I Khodier, Majid M. Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

Design of full-band infinite impulse response (IIR) digital differentiator (DD) and digital integrator (DI) based on the Lk-norm and the min-max optimality criteria have been demonstrated in literature. However, the designed IIR DDs and DIs using these criteria do not have linear phase or have unsatisfactory magnitude response. In this paper, a novel approach to design, optimize and improve the magnitude response and the linearity of the phase response of IIR DD and DI using the cuckoo search (CS) optimization algorithm is proposed. The proposed approach is based on a varied Lk-norm where k linearly increases with the number of the frequency samples between the values 1 and 2. The design and optimization of DDs and DIs are carried out using the CS optimization method due to its simplicity, efficiency, and robustness in solving general multidimensional optimization problems. The performance of the designed DDs and DIs using the proposed approach is compared with designs based on different criteria and algorithms available in literature. The comparison shows that the designed DDs and DIs based on the proposed approach have better phase responses and better or at least comparable magnitude responses compared to those of DDs and DIs designed using other methods in the literature.

关键词： Digital differentiator digital integrator linear phase cuckoo search algorithm optimization

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